Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7

Atanu Roy, Priyanka Bagade

2024

Abstract

The effective and dependable distribution of clean water to communities depends on the timely inspection and repair of water pipes. Traditional inspection techniques frequently require expensive physical labour, resulting in false and delayed defect detections. Current water pipeline inspection methods include radiography testing, eddy current testing, and CCTV inspection. These methods require experts to be present on-site to conduct the tests. Radiographed and CCTV images are usually used for pipeline defect detection on-site, yet real-time automatic detection is lacking. Current approaches, including YOLOv5 models with Retinex-based illumination, achieve acceptable performance but hinder fast inference due to bulky models, which is especially concerning for edge devices. This paper proposes an Attentive-YOLO model based on the state-of-the-art object detection YOLOv7 model with a reduced Efficient Layer Aggregation Network (ELAN). We propose a lightweight attention model in the head and backbone of the YOLOv7 network to improve accuracy while reducing model complexity and size. The paper aims to present an efficient model to be deployed on edge devices such as the Raspberry Pi to be used in Internet of Things (IoT) systems and on-site robotics applications like pipeline inspection robots. Based on the experiments, the proposed model, Attentive-YOLO, achieves an mAP score of 0.962 over 0.93 (1/3rd channel width) compared to the Yolov7-tiny model, with an almost 20% decrease in model parameters.

Paper Citation

in Harvard Style

Roy A. and Bagade P. (2024). Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 492-499. DOI: 10.5220/0012374000003660

in Bibtex Style

@conference{visapp24,
author={Atanu Roy and Priyanka Bagade},
title={Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={492-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012374000003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7
SN - 978-989-758-679-8
AU - Roy A.
AU - Bagade P.
PY - 2024
SP - 492
EP - 499
DO - 10.5220/0012374000003660
PB - SciTePress