A Modular Detection System for Smart Cities: Integrating Monocular

and LiDAR Solutions for Scalable Trafﬁc Monitoring

Javier Borau-Bernad

1 a

Alvaro Ramajo-Ballester

1 b

, Jos

e Mar

ıa Armingol Moreno

1 c

and

Araceli Sanchis de Miguel

2 d

Intelligent Systems Lab, University Carlos III of Madrid, Av. de la Universidad, 30, Legan

es, 28911, Madrid, Spain

Control Learning and Systems Optimization Group, University Carlos III of Madrid, Av. de la Universidad, 30,

Legan

es, 28911, Madrid, Spain

Keywords:

3D Object Detection, Intelligent Infrastructures, Smart Cities, Autonomous Driving, Deep Learning.

Abstract:

As smart cities continue to develop, they require scalable and efﬁcient trafﬁc monitoring systems. This paper

presents a modular detection system that switches between monocular and multimodal modes, depending on

the available sensors. The monocular mode, based on the MonoLSS algorithm, offers a cost-effective vehicle

detection solution using a single camera, ideal for simpler or low-budget setups. In contrast, the multimodal

mode integrates camera and LiDAR data via the MVX-Net model, enhancing 3D accuracy in complex trafﬁc

scenarios. This dual-mode ﬂexibility allows smart cities to adapt the system to their infrastructure and bud-

getary needs, ensuring scalability as urban demands evolve. Inference results demonstrate the superior accu-

racy of the multimodal approach in challenging environments while validating the efﬁciency of the monocular

mode for simpler settings. Therefore, the modular detection system offers a ﬂexible solution that optimizes

both cost and performance, effectively addressing the varied requirements of smart city trafﬁc management.

1 INTRODUCTION

As urban environments continue to grow and become

more complex, the rise of smart cities represents a so-

lution to make them more efﬁcient, sustainable, and

secure, improving the quality of life for their resi-

dents. Smart cities leverage cutting-edge technologies

to create advanced systems that manage resources,

optimize energy usage and enhance transportation

systems (Zanella et al., 2014). Among these systems,

intelligent vehicle detection plays a key role in im-

proving trafﬁc management, advancing autonomous

vehicle capabilities and increasing road safety. How-

ever, many current detection solutions either lack suf-

ﬁcient data quality for real-time analysis or rely on

the widespread adoption of autonomous vehicles in

the trafﬁc system (Jain et al., 2019). Addressing these

limitations requires the development of scalable solu-

tions that can deliver high quality trafﬁc data analysis.

Intelligent infrastructures emerge as a key com-

ponent for trafﬁc monitoring by integrating sensors

and analysis algorithms into urban elements such

https://orcid.org/0009-0009-5623-1688

https://orcid.org/0000-0001-9425-9408

https://orcid.org/0000-0002-3353-9956

https://orcid.org/0000-0002-1429-4092

as trafﬁc cameras and bridge-mounted surveillance

systems. These infrastructure-based sensors offer

a broader ﬁeld of view and reduce occlusions be-

tween vehicles, allowing detection algorithms to sig-

niﬁcantly improve precision (Borau Bernad et al.,

2024). The strategic placement of roadside sensors

also plays a key role in improving detection accuracy

and minimizing blind spots, particularly in urban ar-

eas where efﬁcient coverage is essential(Owais and

Shahin, 2022). This is particularly beneﬁcial in high-

risk areas, like intersections, where dense trafﬁc and

low visibility complicate effective monitoring.

Despite these advantages of intelligent infrastruc-

tures, the challenge of developing a ﬂexible solution

that adapts to different precision requirements appears

in the research paradigm, to guarantee optimal per-

formance across diverse urban settings. Monocular

detection systems present themselves as an afford-

able and simple solution for 3D vehicle detection, but

are challenged by their limitations in depth estimation

and lighting conditions. In contrast, multimodal de-

tection systems, which integrate LiDAR and monoc-

ular technologies, provide higher 3D accuracy but

come with increased installation costs (Wang et al.,

2023). The challenge in this context lies in designing

a ﬂexible solution that can satisfy different installa-

tion needs while balancing cost and performance.

326

Borau-Bernad, J., Ramajo-Ballester, Á., Moreno, J. M. A. and Sanchis de Miguel, A.

A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Trafﬁc Monitoring.

DOI: 10.5220/0013195200003941

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2025), pages 326-333

ISBN: 978-989-758-745-0; ISSN: 2184-495X

This paper presents a modular detection system

designed to operate in two distinct modes, offering

ﬂexibility and adaptability to diverse urban infrastruc-

tures. The system can work as a monocular solution,

using the MonoLSS algorithm (Li et al., 2024), which

provides a cost-effective and simpler approach for ve-

hicle detection. Alternatively, it can operate as a mul-

timodal system, integrating both LiDAR and monoc-

ular data through the MVX-Net model (Sindagi et al.,

2019) to enhance 3D accuracy in more demanding en-

vironments. Unlike previous approaches that rely on

ﬁxed monocular or multimodal conﬁgurations, this

system introduces a novel dynamic mode selection

based on sensor availability. This modular design

enhances the scalability of smart infrastructures, en-

abling cities to deploy the solution based on their spe-

ciﬁc needs, budget and available sensor installations,

making it versatile across different urban settings.

2 RELATED WORKS

The growing interest in developing technologies for

smart cities has fueled the advancement of intelligent

trafﬁc monitoring systems. While monocular meth-

ods provide an affordable and simple solution, they

struggle with depth estimation errors and rely heav-

ily on certain visibility conditions for optimal perfor-

mance. In contrast, LiDAR and multimodal systems

offer improved detection accuracy and perform well

under low visibility conditions, though at a higher

system cost (Arnold et al., 2019). This section re-

views the key advancements in vehicle detection sys-

tems, highlighting state-of-the-art models, their lim-

itations and the need for a ﬂexible modular solution

that can harness the strengths of each approach based

on the speciﬁc needs of intelligent infrastructure.

Monocular detection systems have emerged as an

attractive solution for urban data acquisition due to

their affordability and ease of deployment. These

systems consist of a single-camera setup that cap-

tures 2D images, which are processed by deep learn-

ing algorithms to obtain three-dimensional vehicle

information. The simplicity of monocular systems

makes them suitable for cities with limited installation

budgets, as they only require conventional calibrated

camera setups and a computational unit to process the

information. This cost-effectiveness and their easy in-

tegration into existing infrastructure make monocular

systems an interesting tool for large-scale deployment

in intelligent trafﬁc monitoring solutions.

During the last decade, several deep learning mod-

els have been developed to improve existing monoc-

ular detection algorithms. The SMOKE model (Liu

et al., 2020) is known for its efﬁciency, speed and sim-

plicity, performing 3D inference in a single stage. On

the other hand, PGD algorithm (Wang et al., 2022)

enhances depth estimation by combining probabilis-

tic and geometric methods to improve detection accu-

racy. MonoCon (Liu et al., 2022) leverages additional

contextual information during training, improving de-

tection accuracy in crowded environments. Lastly,

MonoLSS introduces a Learnable Sample Selection

(LSS) module, optimizing the use of features during

3D property learning and uses MixUp3D augmenta-

tion technique to enhance training data, making the

algorithm highly robust against occlusions.

Despite these advancements, monocular detection

systems still face important limitations, mainly due to

errors during depth estimation and their sensitivity to

non-optimal environmental conditions. The process

of inferring three-dimensional information from 2D

images causes the models to have signiﬁcant errors,

which has a direct impact on the ﬁnal accuracy of the

algorithm. In addition, monocular systems are partic-

ularly affected by low visibility situations, such as ad-

verse weather conditions, as well as low light during

night or direct sunlight hitting the camera lens during

sunrise or sunset, notably reducing their capabilities

and precision (Qian et al., 2022). These limitations

establish the need for integration of more robust so-

lutions, leading to the exploration of LiDAR-based

detection systems, which offer enhanced depth per-

ception and accuracy but at a higher cost.

LiDAR-based systems overcome many of the is-

sues of monocular approaches by inferring detection

from 3D point clouds generated by laser pulses, re-

sulting in better depth and position accuracy regard-

less of light conditions, making them more reliable

in complex urban environments. Notable LiDAR-

based models include SECOND (Yan et al., 2018),

which uses sparse convolutions to efﬁciently process

LiDAR point clouds and PointPillars (Lang et al.,

2019), a method that organizes point clouds into verti-

cal columns for faster processing. Another advanced

approach is VoxelNet (Zhou and Tuzel, 2018), which

divides the point cloud space into 3D voxels and ex-

tracts features for object detection.

However, LiDAR systems face signiﬁcant chal-

lenges that limit their widespread adoption in Smart

Cities. These include high sensor costs, complex in-

stallation and maintenance requirements, such as fre-

quent calibration and cleaning, and reduced robust-

ness in adverse weather conditions like heavy rain or

snow (Owais, 2024). Table 1 provides a comparative

analysis of monocular camera systems and LiDAR-

based systems, highlighting the advantages and dis-

advantages of each sensor type.

A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Trafﬁc Monitoring

327

Table 1: Comparison of Camera and LiDAR sensors under different environmental conditions, considering the impact of

adverse conditions, color capabilities, installation complexity and cost (Zhang et al., 2023).

Sensor type Light rain Heavy rain Smog Snow Strong light Color Complexity Cost

Camera Mid High High Mid High Yes Low Low

LiDAR Low Mid Low High Low No High High

Multimodal detection systems combine the

strengths of both cameras and LiDAR, integrating

2D image data with 3D point clouds to achieve more

accuracy and robustness in vehicle detection. These

systems can capture both the visual information

needed for tasks like license plate recognition,

while also beneﬁting from the depth and spatial

accuracy provided by LiDAR. A good example of

this approach is MVX-Net, which fuses camera and

LiDAR data to generate highly accurate 3D object

detections. By combining the complementary fea-

tures of both sensors, multimodal systems overcome

the limitations of using only image or LiDAR solu-

tions, particularly useful in highly demanding trafﬁc

situations with poor visibility or occlusions. Despite

these advantages, the integration of multiple sensors

increases both the hardware costs and the complexity

of the system, presenting challenges for widespread

deployment in cost-sensitive urban infrastructures.

Monocular, LiDAR and multimodal systems each

offer distinct advantages but also come with trade-offs

in cost, complexity and performance. Monocular sys-

tems are affordable but struggle with depth accuracy,

while LiDAR systems are precise but expensive and

limited in capturing visual details like color or license

plates and multimodal systems provide a balance but

increase overall costs. To address these challenges,

this paper proposes a modular detection system that

operates in monocular and multimodal modes, allow-

ing for ﬂexible deployment based on urban infrastruc-

ture needs, balancing cost and accuracy.

3 OUR APPROACH: MODULAR

DETECTION SYSTEM

To address the challenges resulting from varied trafﬁc

demands, this section introduces a modular detection

system for intelligent infrastructures that can operate

in both monocular and multimodal modes, depend-

ing on the inputs received from the sensors. The sys-

tem dynamically selects the appropriate mode based

on real-time data to maximize the effectiveness of the

intelligent infrastructure. This solution provides the

ﬂexibility needed to adapt to different urban environ-

ments while balancing cost and performance require-

ments, guaranteeing scalability for smart cities.

3.1 System Architecture

The modular detection system is designed to efﬁ-

ciently handle both monocular and multimodal data

inputs, enabling it to adapt to various urban infras-

tructure setups. The system autonomously selects

between these modes based on the sensors available

at the infrastructure setup, ensuring optimal opera-

tion regardless of the installed sensors and making it

an adaptable solution for 3D data acquisition. This

adaptability is critical for ensuring that diverse urban

environments, with different levels of technological

infrastructure, can still beneﬁt from the detection sys-

tem’s advanced capabilities.

The system is built around two core components:

one for monocular detection and another for multi-

modal detection, which are automatically selected by

the system depending on the installed sensors. If

only camera data is available, the system activates

the monocular mode, processing the images with a

monocular 3D detection model to perform 3D vehi-

cle detection in cost-effective conﬁgurations. In con-

trast, when both camera and LiDAR are connected,

the system switches to multimodal mode, leverag-

ing enhanced detection capabilities for more complex

situations. This transition from monocular to multi-

modal mode requires minimal infrastructure modiﬁ-

cations. Existing monocular setups can be upgraded

with LiDAR sensors and updating software to enable

multimodal data processing.

The applications of each module vary based on the

needs of the infrastructure environment. Monocular

mode is well-suited for areas with budget constraints

or less complex trafﬁc, such as smaller cities or high-

visibility intersections where camera-based systems

can guarantee accurate vehicle detection. On the other

hand, multimodal mode is designed for more demand-

ing situations, integrating LiDAR and camera data to

provide enhanced detection in complex trafﬁc condi-

tions. This mode is ideal for busy intersections, envi-

ronments with poor visibility, or locations frequently

affected by bad weather conditions.

Additionally, the ﬂexibility and ease of installa-

tion of the modular system provide signiﬁcant advan-

tages for smart cities development. Monocular setups

can be integrated into existing infrastructure cameras,

allowing for affordable deployment and scalability

over time. As cities evolve and develop, the system

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

328

can easily transition from monocular to multimodal

mode without manual intervention, enabling more ad-

vanced detection capabilities without requiring com-

plete infrastructure redesign. This scalability makes

the system a sustainable long-term solution, allow-

ing cities to gradually enhance their trafﬁc monitor-

ing capabilities as resources and infrastructure allow.

The system’s modular design ensures that new tech-

nologies, like AI-based trafﬁc management platforms,

advanced sensor fusion algorithms or next-generation

LiDAR and high resolution camera systems, can be

incorporated with minimal disruption, allowing for

smooth upgrades as sensor technology advances. Fig-

ure 1 illustrates the overall system architecture and

the module selection depending on the input available

from the infrastructure installed sensors.

3.2 Monocular

The monocular detection component of the modular

detection system is based on MonoLSS model (Li

et al., 2024), an advanced and well-proven algorithm

for 3D object detection using images. MonoLSS is

based on CenterNet (Zhou et al., 2019) and ﬁrst cre-

ates a feature map from the image inputs using a back-

bone DLA-34 (Yu et al., 2018). Then, it leverages

a Learnable Sample Selection (LSS) module which

identiﬁes the most relevant features from the image to

improve the precision of vehicle detection. This LSS

module is based in Gumbel-Softmax and a relative

distance sample divider and uses a warm-up schedule

to improve training stability. In addition, MonoLSS

introduces a new data augmentation technique named

MixUp3D, designed to simulate spatial overlap of

objects without introducing depth ambiguity, which

could cause errors in 3D parameter calculations.

The monocular detection module extracts 3D in-

formation, including depth, dimensions and orien-

tation of vehicles, using the MonoLSS model. To

achieve accurate 3D localization, the system relies on

the camera calibration parameters to transform from

image to real-world coordinates. This approach en-

sures consistent performance in real-time trafﬁc sit-

uations, providing reliable 3D information even with

a single camera, making it ideal for low-cost deploy-

ments in less complex trafﬁc environments.

3.3 Multimodal

The multimodal detection branch of the modular

detection system is based on the MVX-Net model

(Sindagi et al., 2019). This algorithm combines Li-

DAR and monocular image data to extract accurate

3D information, reducing false negatives and false

positives compared to other 3D detection approaches.

MVX-Net proposes two data fusion techniques, Vox-

elFusion and PointFusion, to integrate information

from both sensor types at different detection stages.

For the implementation of MVX-Net into the modular

system, PointFusion was selected due to its slightly

better performance compared to VoxelFusion.

PointFusion is an early fusion technique that ﬁrst

uses a 2D detection network to extract the feature

maps from the images. Then, the 3D points are

projected onto the image using the camera calibra-

tion matrix and the image feature of each location

is concatenated to the correspondent LiDAR point.

These combined features are processed through Voxel

Feature Encoding (VFE) layers of VoxelNet (Zhou

and Tuzel, 2018), allowing the neural network to

learn from LiDAR and image data. This early fu-

sion method provides richer information resulting in

higher accuracy during 3D object detection.

The multimodal detection system using MVX-

Net combines LiDAR and image data to utilize the

strengths of both modalities. This fusion approach

ensures improved object detection in complex urban

scenarios, where precision is essential to guarantee

citizen safety and enhance trafﬁc management.

4 EXPERIMENTS

4.1 Dataset

For training and implementing the algorithms that set

up the modular detection system, it is necessary to

select a dataset that includes both image and LiDAR

data and which mirrors the design of the future ap-

plication, in this case, infrastructure-based data. The

DAIR-V2X dataset (Yu et al., 2022) satisﬁes these

requirements, providing data from LiDAR and RGB

cameras captured from vehicles and roadside infras-

tructures. In this work, the infrastructure part of the

dataset, DAIR-V2I, was selected.

The subset originally includes 10,000 images cap-

tured with RGB 1920x1080 cameras, along with point

cloud data from LiDAR sensors which offer a hori-

zontal ﬁeld of view (FOV) of 100° and a range of up

to 280 meters. The dataset contains almost 500,000

labeled annotations, including bounding boxes, ob-

ject classes and calibration ﬁles that provide camera

intrinsic parameters and camera-LiDAR calibration

information. During model training, 7,058 images

and corresponding LiDAR data from the dataset were

used, split into 80% for training, 10% for validation

and 10% for testing, ensuring a diverse data distribu-

tion that enables the model to generalize correctly.

A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Trafﬁc Monitoring

329

Figure 1: Overview of the modular detection system, illustrating the two core components for monocular and multimodal

detection. The system dynamically selects the detection mode based on the available sensor inputs, enabling ﬂexible and

scalable deployment in different urban environments.

4.2 Implementation Details

The algorithms of the modular detection system were

implemented and trained on a single server equipped

with two NVIDIA GeForce GTX 3090 Ti.

The MonoLSS model was implemented using the

RoadVision3D library (Borau Bernad, 2024), which

is a custom library developed in PyTorch designed

speciﬁcally for 3D vehicle detection and trafﬁc mon-

itoring applications. The images from the DAIR

dataset were processed in their original resolution of

1920x1080 pixels and then downsampled by a fac-

tor of 4 during feature extraction. The data aug-

mentation techniques applied include random ﬂip-

ping with a 50% probability, random cropping with

a 50% probability, a maximum scaling factor of 0.4

and image center shifting of 0.1. Additionally, the

MixUp3D augmentation technique was applied with

a 50% probability to simulate overlapping objects.

To evaluate the impact of augmentation techniques,

we conducted an ablation study comparing training

with augmentation techniques against training with-

out augmentation for underrepresented classes like

Pedestrians and Cyclists. Augmentation techniques

signiﬁcantly improved detection accuracy, increasing

AP40 for Pedestrians and Cyclists by 3.6% and 2%,

respectively, under moderate difﬁculty. MonoLSS

training was conducted over 150 epochs, with vali-

dation at intervals every 10 epochs, using the Adam

optimizer. The initial learning rate was set to 0.001,

with a warmup phase during the initial 5 epochs, fol-

lowed by a decay rate of 0.1 at epochs 90 and 120.

For the multimodal detection branch, the MVX-

Net model was trained using the MMDetection3D

(MMDetection3D Contributors, 2020) library, based

in PyTorch, which allows for the integration of Li-

DAR and image data to enhance 3D object detec-

tion. MVX-Net was trained using a voxel size of

[0.05, 0.05, 0.1] and a point-cloud range from [0, -

40, -3] to [100, 40, 1], with images loaded at their

original resolution of 1920x1080. The data augmen-

tation techniques included random resizing of images

between (1536, 864) and (2304, 1296), random ﬂip-

ping with a 50% probability and random shufﬂing of

point cloud data. The training lasted for 60 epochs

with an AdamW scheduler, validating every 5 epochs

and used a cosine annealing learning rate that decayed

from 0.003 to 3 × 10

−6

4.3 Evaluation Metrics

The evaluation of the 3D detection models was con-

ducted using the Average Precision (AP) metric, com-

monly used in algorithm comparison on the KITTI

dataset (Geiger et al., 2012), a standard benchmark

for evaluating 3D object detection models. For

comparing the performance of the trained MonoLSS

and MVX-Net algorithms, we used the AP

metric,

which classiﬁes ground truth objects into three levels

of difﬁculty: easy, moderate and hard, based on fac-

tors such as occlusions, pixel size of bounding boxes

and truncation. AP

is well suited for 3D trafﬁc mon-

itoring applications as it combines recall and preci-

sion into a single metric providing a reliable measure

of overall detection performance across different sce-

narios.

The Intersection over Union (IoU) metric is used

to determine the overlap between predicted and

ground truth bounding boxes. The IoU ﬁlters detec-

tions, ensuring that only those meeting a minimum

overlap threshold are considered in the accuracy cal-

culation. Therefore, the Average Precision at 40 recall

points (AP

) is calculated using the following equa-

tion:

∑

r∈R

P(r), (1)

where R = [r

, r

−r

N−1

, r

2(r

−r

)

N−1

, . . . , r

] deﬁnes

N equally spaced recall values between r

= 0 and

= 1, and P(r) is the maximum precision observed

for any recall value greater than or equal to r.

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

330

4.4 Results

This section presents the performance evaluation of

the modular detection system, examining both the

monocular and multimodal modules. Each model was

tested using the AP

metric for the detection of three

object classes. The evaluation uses Intersection over

Union thresholds of 0.70 for Car and 0.50 for Pedes-

trian and Cyclist to determine true positive detections.

The results, shown in Tables 2 and 3, provide a com-

prehensive overview of how the models handle differ-

ent difﬁculty levels, demonstrating the robustness of

each approach in dynamic urban environments.

Table 2: Results of MonoLSS Model on AP

3D detection

for Car, Pedestrian and Cyclist classes at different difﬁculty

levels (Easy, Moderate and Hard), with IoU thresholds of

0.70 for Car and 0.50 for Pedestrian and Cyclist.

Class Easy Moderate Hard

Car 62.24 52.16 52.08

Pedestrian 15.75 14.70 14.60

Cyclist 39.44 21.04 21.35

Table 3: Results of MVX-Net Model on AP

3D detection

for Car, Pedestrian and Cyclist classes at different difﬁculty

levels (Easy, Moderate and Hard), with IoU thresholds of

0.70 for Car and 0.50 for Pedestrian and Cyclist.

Class Easy Moderate Hard

Car 69.13 56.74 56.78

Pedestrian 63.47 59.14 59.03

Cyclist 61.49 29.36 31.04

The results show that both MonoLSS and MVX-

net perform correctly for most object classes and dif-

ﬁculty levels, proving accurate 3D detection capabil-

ities of the modular system. However, the MonoLSS

model shows limitations in detecting pedestrians

(14.70%) and cyclists (21.04%) at moderate difﬁ-

culty, showcasing the limited reliability of monocu-

lar detection systems for less visible objects. Despite

this, MonoLSS remains accurate for vehicle detec-

tion, making it suitable for low-cost implementations

where cars are the primary concern. Such setups are

appropriate in environments with minimal pedestrian

or cyclist trafﬁc, such as highways or districts farther

from the city center. On the other hand, MVX-Net

shows increased performance in all classes due to its

multimodal input source, which highlights the advan-

tage of incorporating LiDAR sensors into intelligent

infrastructures. Table 4 shows these accuracy im-

provements, particularly for pedestrians and cyclists.

In spite of the increased performance of MVX-Net

due to the integration of LiDAR, MonoLSS remains a

viable and cost-effective solution for simpler applica-

Table 4: Comparison of AP

3D detection performance

for Car, Pedestrian and Cyclist classes at an IoU threshold

of 0.70 for Car and 0.50 for Pedestrian and Cyclist between

MonoLSS and MVX-Net at Moderate difﬁculty level. This

comparison highlights the key role of multimodal detection

in improving reliability, especially for smaller objects like

pedestrians and cyclists.

Class MonoLSS MVX-Net Diff.

Car 52.16 56.74 +4.58

Pedestrian 14.70 59.14 +44.44

Cyclist 21.04 29.36 +8.32

Figure 2: Inference results of the MVX-Net model, illustrat-

ing 3D object detection using LiDAR and monocular image

data, with results projected onto the point cloud and image.

This ﬁgure showcases MVX-Net’s accuracy and robustness

in vehicle detection using multimodal inputs.

Figure 3: Inference results of the MonoLSS model, demon-

strating 3D object detection capabilities using only monoc-

ular image data. This ﬁgure showcases MonoLSS’s reliabil-

ity in vehicle detection, making it suitable for cost-sensitive

scenarios.

tions due to its reliability in vehicle detection. Figures

2 and 3 illustrate the inference results of both models

in real-world conditions. These images highlight their

performance in detecting vehicles as the primary ob-

jective in trafﬁc monitoring scenarios.

A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Trafﬁc Monitoring

331

4.5 Performance Evaluation Under

Challenging Conditions

Despite the overall satisfactory results obtained by

both models, it is insightful to delve into possible traf-

ﬁc scenarios where the multimodal system might per-

form better than the monocular, beneﬁting from the

installed LiDAR sensors. One possible scenario in-

volves high-density trafﬁc situations, with occlusions

and a large number of vehicles, making detection dif-

ﬁcult for a single camera system. This scenario is il-

lustrated in Figure 4, where the multimodal system is

correctly detecting the vehicles while the monocular

one struggles and is not capable of locating all cars

going through the intersection.

Figure 4: Inference results of the MonoLSS model (top) and

MVX-Net (bottom), illustrating the differences in detection

capabilities under high-density trafﬁc conditions.

Similarly, in low-light conditions such as night op-

erations, the monocular approach is challenged due to

limited visibility and low image quality. In contrast,

the multimodal system shows its strength by using Li-

DAR data to maintain detection accuracy despite the

lack of light. Figure 5 shows experimental results in

this scenario, where low visibility signiﬁcantly affects

the monocular system’s performance.

5 CONCLUSIONS AND FUTURE

WORKS

In this work, a modular detection system has been de-

signed for smart cities, capable of operating in intel-

ligent infrastructures equipped with either monocular

or multimodal sensors. This modular conﬁguration

addresses the need for ﬂexibility in trafﬁc monitor-

ing by offering a monocular solution, based on the

Figure 5: Inference results of the MonoLSS model (top) and

MVX-Net (bottom), illustrating the differences in detection

capabilities under low light trafﬁc conditions, where image

data alone is not enough for accurate detection.

MonoLSS algorithm, for simpler environments, while

enhancing detection accuracy and robustness with the

integration of LiDAR data through the multimodal

MVX-Net model. This dual-mode design allows ur-

ban environments to adapt their trafﬁc monitoring

systems to different requirements and resource avail-

ability, balancing performance with cost efﬁciency.

The experiments conducted demonstrate that both

algorithms are able to provide accurate 3D vehicle

detection, ensuring effective trafﬁc monitoring in di-

verse urban environments. The monocular module

performs correctly and accurately in most scenarios

while the multimodal conﬁguration shows enhanced

detection capabilities in more challenging situations,

such as high density trafﬁc or low light environments.

Overall, the results validate the effectiveness of the

modular approach in addressing a range of urban

trafﬁc monitoring needs, demonstrating that both the

monocular and multimodal conﬁgurations work ef-

fectively under their respective conditions.

To advance this work, it is key to carry out real-

world experiments on vehicle detection to validate the

system’s efﬁciency under dynamic and uncontrolled

situations. Based on the results of these experiments,

it will be important to improve the system’s accuracy

and robustness, particularly in classes and situations

where it struggles. Additionally, future studies should

incorporate datasets representing diverse real-world

scenarios, including adverse weather, dense urban ar-

eas and rural regions, to enhance the generalizability

of ﬁndings and validate the system under varied en-

vironmental and trafﬁc conditions. Such tools would

make it easier to identify bottlenecks, optimize trafﬁc

light timing, and enhance overall urban trafﬁc ﬂow,

VEHITS 2025 - 11th International Conference on Vehicle Technology and Intelligent Transport Systems

332

contributing to the development of smart cities. Fur-

thermore, future research could also explore the appli-

cation of the modular detection system beyond trafﬁc

monitoring, such as pedestrian ﬂow analysis, public

safety enhancement or intelligent parking detection.

In conclusion, the modular detection system de-

veloped in this work advances the development of

smart cities by providing a scalable, ﬂexible and

adaptable solution for trafﬁc monitoring. This ap-

proach not only meets current urban mobility de-

mands but also sets a strong foundation for future in-

novations in the intelligent infrastructures ﬁeld, en-

suring that cities can address the emerging challenges

in trafﬁc safety, efﬁciency and sustainability.

ACKNOWLEDGEMENTS

This research was funded by MCIN/AEI/10.13039/

501100011033 grant numbers PID2021-124335OB-

C21, PID2022-140554OB-C32 and PDC2022-

133684-C31.

REFERENCES

Arnold, E., Al-Jarrah, O. Y., Dianati, M., Fallah, S., Ox-

toby, D., and Mouzakitis, A. (2019). A survey on

3d object detection methods for autonomous driving

applications. IEEE Transactions on Intelligent Trans-

portation Systems, 20(10):3782–3795.

Borau Bernad, J. (2024). RoadVision3D. https://github.

com/jborau/RoadVision3D.

Borau Bernad, J., Ramajo-Ballester,

A., and Armin-

gol Moreno, J. M. (2024). Three-dimensional vehicle

detection and pose estimation in monocular images

for smart infrastructures. Mathematics, 12(13):2027.

Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready

for autonomous driving? the kitti vision benchmark

suite. 2012 IEEE Conference on Computer Vision and

Pattern Recognition, pages 3354–3361.

Jain, N. K., Saini, R., and Mittal, P. (2019). A review

on trafﬁc monitoring system techniques. Soft com-

puting: Theories and applications: Proceedings of

SoCTA 2017, pages 569–577.

Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., and

Beijbom, O. (2019). Pointpillars: Fast encoders for

object detection from point clouds. In Proceedings

of the IEEE/CVF conference on computer vision and

pattern recognition, pages 12697–12705.

Li, Z., Jia, J., and Shi, Y. (2024). Monolss: Learnable

sample selection for monocular 3d detection. In 2024

International Conference on 3D Vision (3DV), pages

1125–1135.

Liu, X., Xue, N., and Wu, T. (2022). Learning auxiliary

monocular contexts helps monocular 3d object detec-

tion. In Proceedings of the AAAI Conference on Arti-

ﬁcial Intelligence, volume 36, pages 1810–1818.

Liu, Z., Wu, Z., and T’oth, R. (2020). Smoke: Single-stage

monocular 3d object detection via keypoint estima-

tion. 2020 IEEE/CVF Conference on Computer Vision

and Pattern Recognition (CVPR), pages 4289–4298.

MMDetection3D Contributors (2020). MMDetection3D:

OpenMMLab next-generation platform for general

3D object detection. https://github.com/open-mmlab/

mmdetection3d.

Owais, M. (2024). Deep learning for integrated ori-

gin–destination estimation and trafﬁc sensor location

problems. IEEE Transactions on Intelligent Trans-

portation Systems, 25(7):6501–6513.

Owais, M. and Shahin, A. I. (2022). Exact and heuris-

tics algorithms for screen line problem in large size

networks: Shortest path-based column generation ap-

proach. IEEE Transactions on Intelligent Transporta-

tion Systems, 23(12):24829–24840.

Qian, R., Lai, X., and Li, X. (2022). 3d object detection for

autonomous driving: A survey. Pattern Recognition,

130:108796.

Sindagi, V. A., Zhou, Y., and Tuzel, O. (2019). Mvx-net:

Multimodal voxelnet for 3d object detection. In 2019

International Conference on Robotics and Automation

(ICRA), pages 7276–7282.

Wang, T., Xinge, Z., Pang, J., and Lin, D. (2022). Proba-

bilistic and geometric depth: Detecting objects in per-

spective. In Conference on Robot Learning, pages

1475–1485. PMLR.

Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li,

H., and Zhang, Y. (2023). Multi-modal 3d object de-

tection in autonomous driving: a survey. International

Journal of Computer Vision, 131(8):2122–2152.

Yan, Y., Mao, Y., and Li, B. (2018). Second:

Sparsely embedded convolutional detection. Sensors,

18(10):3337.

Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018).

Deep layer aggregation. In Proceedings of the IEEE

conference on computer vision and pattern recogni-

tion, pages 2403–2412.

Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo,

Z., Li, H., Hu, X., Yuan, J., and Nie, Z. (2022). Dair-

v2x: A large-scale dataset for vehicle-infrastructure

cooperative 3d object detection. In Proceedings of the

IEEE/CVF Conference on Computer Vision and Pat-

tern Recognition (CVPR), pages 21361–21370.

Zanella, A., Bui, N., Castellani, A., Vangelista, L., and

Zorzi, M. (2014). Internet of things for smart cities.

IEEE Internet of Things journal, 1(1):22–32.

Zhang, Y., Carballo, A., Yang, H., and Takeda, K. (2023).

Perception and sensing for autonomous vehicles under

adverse weather conditions: A survey. ISPRS Journal

of Photogrammetry and Remote Sensing, 196:146–

177.

Zhou, X., Wang, D., and Kr

ahenb

uhl, P. (2019). Objects as

points. arXiv preprint arXiv:1904.07850.

Zhou, Y. and Tuzel, O. (2018). Voxelnet: End-to-end learn-

ing for point cloud based 3d object detection. In Pro-

ceedings of the IEEE Conference on Computer Vision

and Pattern Recognition (CVPR).

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