Real-time Distance Measurement in a 2D Image on Hardware

with Limited Resources for Low-power IoT Devices

(Radar Control System)

Jurij Kuzmic and Günter Rudolph

Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, Germany

Keywords: Real-time Distance Measurement, Radar Control System, Autonomous Driving, Object Detection,

Computational Intelligence, Computer Vision.

Abstract: This paper presents an approach for real-time distance measurement in a 2D image on hardware with limited

resources without a reference object. Additionally, different approximated functions for distance

measurement are presented. Here, we focus on an approach to develop real-time distance detection for

hardware with limited resources in the field of the Internet of Things (IoT). Also, our distance measurement

system is evaluated with simulated data, real data from model making area and data from a real vehicle from

real environment. In the beginning, related work of this paper is discussed. The data acquisition of the different

simulated and real data sets is also discussed in this paper. Additionally, dissimilar resolutions for distance

measurement are compared in accuracy and run time to find the better and faster system for distance

measurement in a 2D image on hardware with limited resources for low-power IoT devices. Through the

experiments described in this paper, the comparison of the run time depending on different IoT hardware is

presented. Here, the idea is to develop a radar control system for self-driving cars from model making area

and vehicles from real environment. Finally, future research and work in this area are discussed.

1 INTRODUCTION

Autonomous vehicles are being developed daily in

the autonomous vehicle industry. This development

includes the installation of various sensors and new

electronic components. The autonomous vehicle

needs these sensors to interpret their environment and

interact with it accordingly. In this way the

autonomous vehicles are able to drive independently

without human intervention. But how does the

autonomous vehicle behave if these sensors fail or

provide incorrect measurements? The radar sensor

can measure the distance to the object in front using

electromagnetic waves. Thereby, an autonomous

vehicle is able to keep the distance to the vehicle in

front, acts as a braking or emergency braking assistant

automatically. To check these measurements, this

paper presents an optical control system for the radar

sensor. This radar control system is realised by optical

distance measurement in a 2D image. Also, the stereo

camera can be used for this purpose. This camera

contains two cameras at a certain distance, similar to

the human eye. This delivers two images. These both

images can be used to determine the depth of the

image and distinguish between roads, people, cars,

houses, etc. (Li, Chen and Shen, 2019). Our approach

is to develop this optical radar control system with

only one camera and to perform the distance

measurements to the front object in a 2D image. With

one camera there is only half as much input data to

process. This approach makes it possible to check the

measured values of the radar sensor. Furthermore, our

system operates without a reference object, which is

needed to convert the pixels to the real distance in the

real environment. We emphasize that this system is

not a replacement for the radar sensor, rather it is

intended to serve as an optical distance verification

system.

In order to implement the distance measurements,

the position of the front object must also be known.

This object detection has to be done in real time, as

the distance measurement has also to be done in real

time. Also, the position of these objects in a 2D image

is very important. For example, is the vehicle in front

on the same lane or is it just parked on the side of the

road? Here, a lane detection can be advantageous. To

Kuzmic, J. and Rudolph, G.

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System).

DOI: 10.5220/0011188100003277

In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications (DeLTA 2022), pages 94-101

ISBN: 978-989-758-584-5; ISSN: 2184-9277

detect the lane in real time, we have already presented

the filtered Canny edge detection algorithm (Kuzmic

and Rudolph A1, 2021).

The future goal of our work is to switch from the

simulation we developed before (Kuzmic and

Rudolph, 2020) to the real model cars. In case of a

successful transfer of simulation to reality (sim-to-

real transfer), the model car behaves exactly as before

in the simulation. Here, the hardware of these model

cars belongs to the low-power IoT devices with

limited resources.

2 RELATED WORK

There are some scientific papers dealing with distance

measurement, e.g. (Marutotamtama and Setyawan,

2021) who have made a physical distancing detection

using YOLO v3 and bird's eye view transform or

(Rahman et al., 2009) who have developed a person

to camera distance measurement based on eye-

distance. Furthermore, there are (Mahammed,

Melhum and Kochery, 2013) who implemented an

object distance measurement by stereo vision. This

approach requires a 3D camera consists of two

cameras of parallel optical axes. However, there also

some scientific papers that are dealing with the

detection of the objects in real time on hardware with

limited resources for low-power IoT devices. For

example, (Wang, Li and Ling, 2018) who have

developed pelee: a real-time object detection system

on mobile devices. This system reaches 23.6 FPS on

an iPhone 8. Similarly, there are (Jose et al., 2019)

who have researched real-time object detection on

low power embedded platforms. This system operates

at 22 FPS on low-power TDA2PX System on Chip

(SoC) provided by Texas Instruments (TI).

Additionally, there are scientific works that deal with

YOLO real-time object detection for low-power

hardware, such as (Huang, Pedoeem and Chen, 2018)

who have developed YOLO-LITE: a real-time object

detection algorithm optimized for non-GPU

computers or (Jin, Wen and Liang, 2020) who

implemented embedded real-time pedestrian

detection system using YOLO optimized by LNN.

Our approach is to implement a distance

measurement with only one camera with a function

approximation (without a reference object) and to

develop a real-time radar control system for low-

power IoT hardware. Thus, it is possible to develop a

low-cost real-time distance measurement e.g. for

model making or a surveillance camera in a short

time. For this purpose, we use the Raspberry Pi 3 B

and Raspberry Pi 4 B.

3 DATA SETS

Before distance measurements can be conducted,

different data sets are needed. A higher quality of data

increases the chance for a successful distance

measurement with a low error rate. These data sets are

also the basis for a successful distance measurement.

For this purpose, we created different data sets from

simulation, model making area and real environment.

Data from the simulation could be automatically

generated and annotated with our already published

simulator (Kuzmic and Rudolph, 2020). Data from

the model making area and the real environment has

to be created and annotated manually. These

procedures are described in sections 3.1 and 3.2. The

resolution is given in the format width × height. Data

set 1 contains images of a simulation car with the

corresponding distances (Unity unit) with 795

pictures. Data set 2 includes data of a model car

(PiCar) from the model making area with the

corresponding distances (metres) with 30 pictures.

Data set 3 contains images of a real vehicle from the

real world with the corresponding distances (metres)

with 24 pictures. Figure 1 shows some images from

our data sets. The resolution of data sets 1 and 2 is

1280 × 720 pixels. In figure 1, third row can also be

seen that the images are smaller in height. The

resolution of data set 3 is 1280 × 500 pixels. The

engine hood of our real vehicle was cut out from these

pictures, so the pictures were reduced in height.

Figure 1: Our data sets for distance measurement. First row:

Data set 1 from simulator with simulation car. Second row:

Data set 2 from model making area with model car (PiCar).

Third row: Data set 3 from real environment with real

vehicle.

Here, this resolution is completely sufficient. In the

next step the pictures of our data sets will be resized

to the resolution of 320 × 160 pixels. This will be

discussed in section 4. Table 1 shows the data sets we

have created for our distance measurement including

the description and unit scale. For each picture there

is a corresponding distance (unit scale), which is

relevant for the later distance conversion.

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)

Table 1: Data sets for distance measurement. Count stands

for the number of records.

No. Name Description Unit scale Count

1 Sim

Distances

from Unity

3D simulator

Unity unit 795

2 Mod

Distances

from model

making area

Meter 30

3 Real

Distances

from real

environment

Meter 24

3.1 Automatic Annotation

One of the advantages of working in the simulated

environment is the rapid generation of automatically

annotated data (Kuzmic and Rudolph A2, 2021).

Through rapid prototyping, meaningful results can be

generated quickly without an elaborate experimental

setup in the real environment. To obtain the

automatically annotated simulation data for the

distance measurement, our previously published

simulator applied to accident simulations and

emergency corridor building on motorways was used

(Kuzmic and Rudolph, 2020). The corresponding

distance (unit scale in table 1) to the simulation car in

front was measured with virtual ray casts (Fig. 2).

Figure 2: Automatic data acquisition and annotation with

the Unity 3D simulator. Yellow line demonstrates the ray

cast for the distance measurement (no. 1 in table 1).

Ray casts can be imagined as a line in a certain

direction with a certain length. This ray is shown as a

yellow line between the red and grey simulation car

in figure 2. So, the distance between both objects can

be determined. The distance is given in Unity units

(no. 1 in table 1). Thus, an image with the

corresponding distance could be automatically

generated and saved. The different objects can also be

exchanged without much effort. Consequently, the

data set from the simulation can be extended as

desired and automatically annotated. This is an

additional advantage of the simulated environment.

3.2 Manual Annotation

To generate the data set from the model making area

for a model car, a small test track was set up with two

model cars (Fig. 3, top). Each of these model cars

have a camera installed in front. This makes it

possible to get pictures at a certain distance from the

model car in front of it. These model cars were placed

at certain distances from each other. First, the

distance to the front model car was measured with a

tape measure (Fig. 3, top, orange circle). Then the

model car behind was used to take a picture from the

model car in front manually. Therefore, a 2D input

image with a corresponding distance as annotation

(no. 2 in table 1) can be obtained. A similar procedure

was realised with the real vehicles from the real

environment (no. 3 in table 1). These pictures were

also taken with the model car, which was on the roof

of the real vehicle (Fig. 3, bottom, green circle). This

model car was also aligned parallel to the road. The

distance measurement was also carried out with a tape

measure (Fig. 3, bottom, orange circle). An

alternative could be a dashcam on the windscreen of

the real vehicle. This could also provide necessary

pictures.

Figure 3: Manual data acquisition and annotation. Top:

Distance measurements with model cars. Bottom: Distance

measurements with real vehicles.

4 DISTANCE MEASUREMENT

After the data acquisition, the distance measurement

in a 2D image with simulation, model making and

real environment data could be started. Our approach

is to realise the distance measurements without a

reference object. Thereby, the real size of an object in

DeLTA 2022 - 3rd International Conference on Deep Learning Theory and Applications

the same image is not needed. Here, our approach is

to approximate a mathematical function to converting

the measured pixels to the real distance. We have

considered two approaches for the distance

measurement with a bird's eye view transformation

and without a bird's eye view transformation for the

simulation data, data from the model making area and

data from the real world. This transformation yields a

view of the lane from the top (Venkatesh and

Vijayakumar, 2012). The pixel measurements for the

distance start at the bottom of the image in each case.

Additionally, the input image was resized to a

resolution of 320 × 160 pixels. This resolution has

already been used in our real-time lane detection with

the filtered Canny edge detection algorithm (Kuzmic

and Rudolph A1, 2021). For this reason, we use this

resolution for distance measurement. Thus, the

distance of the vehicles on the same lane can be

measured in the next step.

Some preliminary experiments show: The

conversion of the pixels to the actual distance

depends on the resolution of the input image, the

inclination of the camera and the height of the camera

from the road. If these match, these approximated

functions can be used. So, the autonomous vehicle

can be calibrated once in the factory before delivery.

The pixel calculation is done as a Euclidean distance

(Malkauthekar, 2013) in pixels to the vehicle in front

in a 2D image. Here, a linear distance measurement

to the object in front was carried out. If the road is

curved, the linear distance measurement can be

performed, too. The distance measurement is carried

out up to the bottom line of the detected object using

our real-time object detection for hardware with

limited resources for low-power IoT devices (Kuzmic

and Rudolph, 2022). Thus, the bounding box of the

object did not have to be defined manually.

The source (src) and destination (dst) parameters

required for the bird's eye view transformation to

generate the transformation matrix were found by

trial and error. We add them for completeness for the

320 × 160 pixel images:

src = [[0, 0], [320, 0], [320, 135], [0 , 135]]

dst = [[0, 0], [320, 0], [176, 135], [144, 135]]

For the real environment data with real vehicles, the

destination changes to:

dst = [[0, 0], [320, 0], [163.2, 135], [156.8, 135]]

4.1 Simulation

The distance measurement was performed on the

simulation data to check whether an optical distance

measurement without a reference object is possible at

all. As already mentioned, a real-time object detection

is the condition for a real-time distance measurement.

To measure the distance in pixels in a 2D image, the

position of the object in pixels have to be known in

this 2D image. First, we started with the bird's eye

view transformation (Venkatesh and Vijayakumar,

2012). Figure 4 (left) shows the input image with

object detection and distance measurement for the

simulation data set. Figure 4 (right) represents the

bird's eye view transformation for the simulation data

set. The red line shows the Euclidean distance to the

detected object in pixels (Fig. 4, right). The converted

input image into the bird's eye view is smaller in

height. This is a result of the transformation. The

position of the object can also be converted with the

generated transformation matrix from the bird's eye

view transformation. The object detection is done on

the original input image. Therefore, the conversion of

the both bottom coordinates of the bounding box into

the bird's eye view is sufficient. Figure 4 shows the

conversion of all pixels of the input image including

the position of the object into the bird's eye view. The

conversion of all pixels is only for a better

representation and is excluded from the run time

measurements in section 5.2.

Figure 4: Bird’s eye view transformation for simulation

data. Left: Input image with object detection and measured

distance in pixels. Right: Transformed bird’s eye view.

After the successful calculation of the Euclidean

distance in pixels, a mathematical linear function can

be approximated. This linear function (Fig. 5, green

graph) is used to convert the calculated Euclidean

distance into the actual real distance. In simulation

data the measured value is Unity unit. The points in

blue show the real measured distances from data set

1. Figure 5 shows this approximated mathematical

linear function for the data set from the simulation.

Here, the total measured distance in the simulated

environment is 8.5 Unity units. Figure 6 shows the

Euclidian distance in pixels in a 2D input image

without the transformation into the bird's eye view.

As can be seen, the approximated mathematical

function is a part of a polynomial graph of degree 6.

With increasing distance in Unity unit, there are fewer

pixels for distance measurement. This means some of

the pixels are the same for different distances. For this

reason, we only continue with the bird's eye view

transformation approach for distance measurement.

The mathematical linear function (Fig. 5) is easier to

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)

approximate than the polynomial graph of degree 6

(Fig. 6).

Figure 5: Function approximation with bird’s eye view in a

320 × 160 pixel image for simulation data. Green line:

Graph of an approximated linear function. Blue points: Real

measured distances from data set 1 (Sim).

Figure 6: Function approximation without bird’s eye view

in a 320 × 160 pixel image for simulation data. Green line:

Graph of an approximated polynomial function of degree 6.

Blue points: Real measured distances from data set 1 (Sim).

4.2 Model Making Area

Similar to the data from the simulation, the distance

measurement was also conducted for data set 2 (Mod)

from the model making area. The procedure for the

distance measurement with bird’s eye view

transformation is the same. First, the position of the

front object in the 2D input image have to be known.

Then the input image can be transformed into the

bird's eye view. With the transformation matrix

generated from the bird's eye view, the position of the

object can be converted. Figure 7 (right) shows the

bird's eye view transformation for data set 2 (Mod).

The red line represents the Euclidean distance to the

detected object in pixels. Figure 7 (left) shows the

input image including object detection and distance

measurement for data from model making area. Here,

the total measured distance in the model making area

is 1.37 metres. Here, a mathematical linear function

can be successfully approximated (Fig. 8, green line)

as well. This function can be used to convert the

pixels into the actual distance, in metres.

Figure 7: Bird’s eye view transformation for data from

model making area. Left: Input image with object detection

and measured distance in pixels. Right: Transformed bird’s

eye view.

Figure 8: Function approximation with bird’s eye view in a

320 × 160 pixel image for model making data. Green line:

Graph of an approximated linear function. Blue points: Real

measured distances from data set 2 (Mod).

4.3 Real Environment

After successfully distance measurement for the data

from the simulation and the model making area, the

data set 3 (Real) from the real environment with real

vehicles was investigated. In our development, we

focused on the simulation and model making area.

However, it is also interesting to investigate distance

measurement with real vehicles. Figure 9 illustrates

this distance measurement in use for data set 3 (Real).

Figure 9 (left) shows the input image including object

detection and distance measurement for data from the

real environment with real vehicles. Figure 9 (right)

represents the transformed bird’s eye view image

from the real environment. The source (src) and

destination (dst) parameters required for the bird's eye

view transformation were found by trial and error and

are included for completeness for the 320 × 160 pixel

images:

src = [[0, 0], [320, 0], [320, 135], [0 , 135]]

dst = [[0, 0], [320, 0], [163.2, 135], [156.8, 135]]

Figure 10 depicts the approximated mathematical

linear function for converting pixels to the actual

distance (green line). The distance output is given in

metres in this scenario. Here, the total measured

distance in the real environment is 60 metres.

DeLTA 2022 - 3rd International Conference on Deep Learning Theory and Applications

Figure 9: Bird’s eye view transformation for data from real

environment. Left: Input image with object detection and

measured distance in pixels. Right: Transformed bird’s eye

view.

Figure 10: Function approximation with bird’s eye view in

a 320 × 160 pixel image for real environment data. Green

line: Graph of an approximated linear function. Blue points:

Real measured distances from data set 3 (Real).

5 EXPERIMENTS

The following experiments were carried out to

compare the measurement error and the run time of

the different resolution and different IoT hardware.

The resolution is in the format width × height. All

experiments were carried out on the same hardware

and input images. For hardware with limited

resources, a single-board Raspberry Pi 3 B and

Raspberry Pi 4 B were used. The run time of distance

measurement is shown in milliseconds (ms). These

measurements contain only the time for distance

measurement and do not include the time for loading

the input images, the time for lane or object detection.

Our hardware for the experiments:

 Raspberry Pi 3 B with ARM Cortex-A53 1.2

GHz CPU, 1 GB RAM, USB 2.0, 8 GB SD as

hardware with limited resources.

 Raspberry Pi 4 B with ARM Cortex-A72 1.5

GHz CPU, 8 GB RAM, USB 3.0, 16 GB SD as

hardware with limited resources.

5.1 Different Resolution and Accuracy

In these experiments, different resolutions for the

input images in the distance measurement are

investigated. To check the quality of the optical radar

control system and the distance measurement for

different resolutions, the mean absolute error (MAE)

for the measurement error was calculated (Willmott

and Matsuura, 2005). The measurement error is given

in metres (m). Here, it was assumed that the unit scale

from the simulation (Unity unit) is equal to the unit

scale from the real environment (meter).

Additionally, the MAE value was converted to

percentage of the total distance (MAE in %). Through

this percentage conversion, the MAE of 0.02 from

exp. no. 6 in table 2 is comparable with MAE of 0.88

from exp. no. 9 in table 2. Data set 1 (Sim) provides

the total distance of 8.5 Unity units. Data set 2 (Mod)

has a total distance of 1.37 metres. Data set 3 (Real)

provides a total distance of 60 metres. The measured

values are rounded to two decimal places.

The results in table 2 show: With greater

resolution in height, there are more pixels for distance

measurement. So, there are fewer duplicate pixels for

different actual distances. For this reason, the mean

absolute error (MAE) is smaller with greater

resolution in height (comparison between exp. no. 1

to 3 and 7 to 9 in table 2).

Table 2: Resolution and measurement error overview of

distance measurement. First column contains the number

(ID) of the experiment (Exp. No.).

Exp.

No.

Resolution

Data

Set

Measurement Error

MAE [m] MAE [%]

320

160

Sim 0.09 1.06

320

Sim 0.09 1.06

320

640

Sim 0.08 0.94

320

160

Mod 0.02 1.46

320

Mod 0.02 1.46

320

640

Mod 0.02 1.46

320

160

Real 0.97 1.62

320

Real 0.89 1.48

320

640

Real 0.88 1.47

5.2 Different Resolution and Run Time

To find a suitable approach for distance measurement

on hardware with limited resources, the run time

should be considered in relation to the measurement

error. These run time measurements were performed

on low-power IoT devices (Raspberry Pi 3 B and 4

B). Small preliminary experiments show: The

duration of the calculation of the Euclidean distance

do not depends on the actual distance of the object in

the input image. Thus, the object was hard coded at

the upper edge of the input image. So, the Euclidean

distance was kept the same for all data sets to compare

the results afterwards. The average run time on

different hardware was calculated from the respective

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)

data set. These run time measurements only include

the conversion of both bottom coordinates of the

object into the bird's eye view, calculation of the

Euclidean distance to the object and the conversion of

the Euclidean distance to the actual distance. For this

purpose, the approximated linear functions already

presented in section 4 are used. The run time

measurements do not include the transformation of

the input image into the bird's eye view as this

transformation is not required for the distance

measurement. The following table 3 summarizes the

resolution and the run time of the distance

measurement.

Table 3: Resolution and run time overview of distance

measurement on Raspberry Pi 3 B and Raspberry Pi 4 B.

First column contains the number (ID) of the experiment

(Exp. No.)

Exp.

No.

Resolution

Data

Set

Run time [ms]

RPI 3 B RPI 4 B

320

× 160

Sim 0.7 0.4

320

× 320

Sim 0.7 0.4

320

× 640

Sim 0.7 0.4

320

× 160

Mod 0.7 0.4

320

× 320

Mod 0.7 0.4

320

× 640

Mod 0.7 0.4

320

× 160

Real 0.7 0.4

320

× 320

Real 0.7 0.4

320

× 640

Real 0.7 0.4

5.3 Evaluation of Run Time and

Accuracy

After the experiments and the performance tests have

been completed, the evaluation of the run times and

the measurement errors can be started. Therefore, it is

important to find a balance between sufficient

accuracy and the run time to find a suitable optical

control system for the radar sensor. The distance

measurement has been successfully investigated and

can be applied on the simulation data, on the data

from model making area and on the data from real

environment for real autonomous vehicles. Here, the

measurement error (MAE in %) varies between 0.94

and 1.62 % (exp. no. 1 to 9 in table 2). This error is

completely sufficient for our purpose. Evidently, if

the resolution increases, the run time of the distance

measurement does not increase, because the pixels of

the input image are not transformed into the bird's eye

view (see e.g. exp. no. 7 and 9 in table 3).

The reviews helped to improve the run time of the

distance measurement.

Additionally, the Raspberry Pi 3 B is slower in

distance measurement than the Raspberry Pi 4 B (exp.

no. 1 in table 3). This is caused by the less powerful

hardware. Next, we consider the distance

measurement with the input images from data set 2

(Mod) with resolution of 320 × 160 pixels on the

Raspberry Pi 4 B. This requires about 0.4 ms for the

distance measurement (exp. no. 4 in table 3). The

error in this optical distance measurement is approx.

1.46 % (exp. no. 4 in table 2). So, a deviation of

approx. 15 cm at 10 m is given. At this point, the

variance of the distance measurement also depends on

the accuracy of the object detection system. More

accurate object detection, gives more accurate

distance measurement to this object. Here, the

position of the bottom line of the object is very

important. Since, the Euclidean distance is measured

exactly up to this line.

On condition that our real-time object detection

for hardware with limited resources for low-power

IoT devices with about 30 frames per second (FPS) is

used (Kuzmic and Rudolph, 2022), the actual run

time for distance measurement can be calculated

depending on this object detection. Each frame is

slowed down by approximately 0.4 ms on a

Raspberry Pi 4 B and about 0.7 ms on a Raspberry Pi

3 B. This reduces the actual frame rate to roughly 29

FPS. So, all our approaches achieve a real-time

distance measurement on our hardware with limited

resources.

6 CONCLUSIONS

This section summarizes once again the points that

were introduced in this paper. In our research, we

focused on real-time distance measurement to

develop an optical radar control system with a single

camera. The usage on hardware with limited

resources for low-power IoT devices was our first

priority. For this purpose, we also created our own

data sets from the simulation, from the model making

area and also from the real environment with a real

vehicle to assess the quality of the sim-to-real

transfer. In addition, several different resolutions and

approximated mathematical linear functions were

analysed to find a balance between sufficient

accuracy and the run time of the distance

measurement. The distance measurement requires

about 0.4 milliseconds with a 1.46 % error for the data

set from the model making area. In our development,

DeLTA 2022 - 3rd International Conference on Deep Learning Theory and Applications

100

we focus on the simulation and model making area.

The approach with real vehicles should show that the

distance measurement is also suitable for the real

world use. At this point, if this distance measurement

is applied to real autonomous vehicles we recommend

to obtain even more data and conducting more

experiments. In conclusion, based on our experiments

the distance measurement without a reference object

conducts successfully in simulation, in model making

and in the real environment. Consequently, an optical

real-time control system for the radar sensor could be

successfully developed. This real-time radar control

system achieves an effective balance between

accuracy and run time.

7 FUTURE WORK

As already announced, the goal of our future work is

to successfully conduct a sim-to-real transfer,

including our real-time lane detection, real-time

object detection and real-time distance measurement

(optical radar control system) we have developed for

the model making area. This means the simulated

environment is completely applied to a real model

vehicle. In this approach, we focus on developing

software for hardware with limited resources for low-

power IoT devices. Additionally, we want to set up a

model test track like a real motorway for this

experiment. Another important aspect on the

motorways is the creation of an emergency corridor

for the rescue vehicles in the case of an accident.

Thus, the behaviour of the vehicles in the simulation

can be compared with the behaviour of the model

vehicles in reality. It is also conceivable to extend this

real-time distance measurement system by a distance

measurement to the detected objects outside the lane.

Therefore, it is possible to track the course of

different objects outside the lane, too. This can be

used, for example, to extend the functionality of the

radar sensor in self-driving cars.

REFERENCES

Huang, R., Pedoeem, J., Chen, C., 2018. YOLO-LITE: A

Real-Time Object Detection Algorithm Optimized for

Non-GPU Computers. IEEE International Conference on

Big Data (IEEE Big Data 2018), ISBN 978-1-5386-

5036-3.

Jin, Y., Wen, Y., Liang, J., 2020. Embedded Real-Time

Pedestrian Detection System Using YOLO Optimized by

LNN. International Conference on Electrical,

Communication, and Computer Engineering (ICECCE),

ISBN 978-1-7281-7117-3.

Jose, G., Kumar, A., Kruthiventi, S., Saha, S., Muralidhara,

H., 2019. Real-Time Object Detection On Low Power

Embedded Platforms. IEEE/CVF International

Conference on Computer Vision Workshop (ICCVW),

ISBN: 978-1-7281-5024-6.

Kuzmic, J., Rudolph, G., 2020. Unity 3D Simulator of

Autonomous Motorway Traffic Applied to Emergency

Corridor Building. In Proceedings of the 5th

International Conference on Internet of Things, Big Data

and Security, ISBN 978-989-758-426-8, pp. 197-204.

Kuzmic, J., Rudolph, G., A1, 2021. Comparison between

Filtered Canny Edge Detector and Convolutional Neural

Network for Real Time Lane Detection in a Unity 3D

Simulator. In Proceedings of the 6th International

Conference on Internet of Things, Big Data and Security

(IoTBDS), ISBN 978-989-758-504-3, pp. 148-155.

Kuzmic, J., Rudolph, G., A2, 2021. Object Detection with

TensorFlow on Hardware with Limited Resources for

Low-Power IoT Devices. 13th International Conference

on Neural Computation Theory and Applications

(NCTA), ISBN 978-989-758-534-0, pp. 302-309.

Kuzmic, J., Rudolph, G., 2022. Real-time Object Detection

with Intel NCS2 on Hardware with Limited Resources for

Low-power IoT Devices. In Proceedings of the 7th

International Conference on Internet of Things, Big Data

and Security (IoTBDS).

Mahammed, M., A., Melhum, A., I., Kochery, F., A., 2013.

Object Distance Measurement by Stereo VISION.

International Journal of Science and Applied Information

Technology (IJSAIT), ISSN: 2278-3083, Volume 2,

Issue 2, pp. 5-8.

Malkauthekar, M. D., 2013. Analysis of euclidean distance

and Manhattan Distance measure in face recognition.

3rd International Conference on Computational

Intelligence and Information Technology (CIIT 2013),

ISBN: 978-1-84919-859-2.

Marutotamtama, J., C., Setyawan, I., 2021. Physical

Distancing Detection using YOLO v3 and Bird's Eye

View Transform. 2nd International Conference on

Innovative and Creative Information Technology

(ICITech), ISBN: 978-1-7281-9748-7.

Rahman, K., A., Hossain, M., S., Bhuiyan, M., A.-A., Zang,

T., Hasanuzzaman M., Ueno, H., 2009. Person to

Camera Distance Measurement Based on Eye-Distance.

3rd International Conference on Multimedia and

Ubiquitous Engineering (MUE 2009), ISBN: 978-0-

7695-3658-3, pp. 137-141.

Venkatesh, M., Vijayakumar, P., 2012. A Simple Bird’s Eye

View Transformation Technique. International Journal of

Scientific & Engineering Research, ISSN: 2229-5518,

Volume 3, Issue 5.

Wang, R. J., Li, X., Ling, C. X., 2018. Pelee: A Real-Time

Object Detection System on Mobile Devices. 32nd

Conference on Neural Information Processing Systems

(NeurIPS).

Willmott, C., J., Matsuura, K., 2005. Advantages of the mean

absolute error (MAE) over the root mean square error

(RMSE) in assessing average model performance. Inter-

Research Science Publisher, ISSN: 0936-577X, Volume

30, Issue 1, pp. 79-82.

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)

101