YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
Source abstract
Abstract
In the field of defect detection in sewers, some researches focus on high accuracy. However, it is challenging for portable on-site devices to provide high performance. This paper proposes a lightweight sewer defect detection model, You Only Look Once (YOLO) v5-Sewer. Firstly, the backbone network of YOLOv5s is replaced with a stacked MobileNetV3 block. Secondly, the C3 module of the neck of YOLOv5s is improved with a C3-Faster module. Thirdly, to compensate for the accuracy loss due to the lightweight network, a channel attention (CA) and convolutional block attention module (CBAM) are added to the proposed method. Finally, the Efficient Intersection over Union (EIOU) is adopted as the localization loss function. Experimental validation on the dataset shows that YOLOv5-Sewer achieves a 1.5% reduction in mean Average Precision (mAP) while reducing floating-point operations by 68%, the number of parameters by 55%, and the model size by 54%, compared to the YOLOv5s model. The detection speed reaches 112 frames per second (FPS) with the GPU (RTX 3070Ti). This model successfully implements a lightweight design while maintaining the detection accuracy, enhancing its functionality on low-performance devices.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Image Enhancement Techniques · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Funding
Beijing Advanced Innovation Center for Intelligent Robots and Systems · BUCEA Post Graduate Innovation Project
References (37)
Show source-provided references
1. Automated defect detection tool for closed circuit television (cctv) inspected sewer pipelines10.1016/j.autcon.2018.01.004 ↗
2. Zhang, J., Liu, X., Zhang, X., Xi, Z., and Wang, S. (2023). Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques. Appl. Sci., 13.10.3390/app13074589 ↗
3. Acoustic and ultrasonic techniques for defect detection and condition monitoring in water and sewerage pipes: A review10.1016/j.apacoust.2021.108282 ↗
4. Li, Y., Wang, H., Dang, L.M., Song, H.K., and Moon, H. (2022). Vision-based defect inspection and condition assessment for sewer pipes: A comprehensive survey. Sensors, 22.10.3390/s22072722 ↗
5. Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M., and Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications—A survey. Sensors, 20.10.3390/s20051459 ↗
6. Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city10.1007/s11783-019-1102-y ↗
7. Automated detection of fault types in CCTV sewer surveys10.2166/hydro.2018.073 ↗
8. Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks10.1016/j.autcon.2018.03.028 ↗
9. Chen, K., Hu, H., Chen, C., Chen, L., and He, C. (2018, January 11–13). An intelligent sewer defect detection method based on convolutional neural network. Proceedings of the IEEE International Conference on Information and Automation, IEEE, Wuyishan, China.10.1109/ICInfA.2018.8812445 ↗
10. A unified convolutional neural network integrated with conditional random field for pipe defect segmentation10.1111/mice.12481 ↗
11. Automatic sewer pipe defect semantic segmentation based on improved U-Net10.1016/j.autcon.2020.103383 ↗
12. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques10.1016/j.autcon.2018.08.006 ↗
13. Wang, M., and Cheng, J.C. (2018, January 10–13). Development and improvement of deep learning based automated defect detection for sewer pipe inspection using faster R-CNN. Proceedings of the Advanced Computing Strategies for Engineering, Lausanne, Switzerland.10.1007/978-3-319-91638-5_9 ↗
14. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
15. Liu, R., Ren, C., Fu, M., Chu, Z., and Guo, J. (2022). Platelet detection based on improved yolo_v3. Cyborg Bionic Syst., 2022.10.34133/2022/9780569 ↗
16. Chen, K., Li, H., Li, C., Zhao, X., Wu, S., Duan, Y., and Wang, J. (2022). An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5. Sensors, 22.10.3390/s22207907 ↗
17. Zhang, X., Zhang, J., Tian, L., Liu, X., and Wang, S. (2023). A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5. Appl. Sci., 13.10.3390/app13158986 ↗
18. Faster r-cnn: Towards real-time object detection with region proposal networks
19. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the European Conference Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
20. Patil, R.R., Calay, R.K., Mustafa, M.Y., and Ansari, S.M. (2023). AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems. Electronics, 12.10.3390/electronics12173606 ↗
21. Wang, T., Li, Y., Zhai, Y., Wang, W., and Huang, R. (2023). A Sewer Pipeline Defect Detection Method Based on Improved YOLOv5. Processes, 11.10.3390/pr11082508 ↗
22. Huang, Q., Zhou, Y., Yang, T., Yang, K., Cao, L., and Xia, Y. (2023). A Lightweight Transfer Learning Model with Pruned and Distilled YOLOv5s to Identify Arc Magnet Surface Defects. Appl. Sci., 13.10.3390/app13042078 ↗
23. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the International Conference on Computer Vision, Seoul, Republic of Korea.
24. Chen, J., Kao, S.h., He, H., Zhuo, W., Wen, S., Lee, C.H., and Chan, S.H.G. (2023, January 17–24). Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.01157 ↗
25. Yu, J., Wang, C., Xi, T., Ju, H., Qu, Y., Kong, Y., and Chen, X. (2023). Development of an Algorithm for Detecting Real-Time Defects in Steel. Electronics, 12.10.3390/electronics12214422 ↗
26. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
27. Hou, Q., Zhou, D., and Feng, J. (2021, January 20–25). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01350 ↗
28. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
29. Yougao, L., and Wei, H. (2019, January 18–20). Identification and feature extraction of drainage pipeline cracks based on SVD and edge recognition method. Proceedings of the Electronic Information Technology and Computer Engineering, IEEE, Xiamen, China.10.1109/EITCE47263.2019.9095153 ↗
30. Wang, Z., Jin, L., Wang, S., and Xu, H. (2022). Apple stem/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loading system. Postharvest Biol. Technol., 185.10.1016/j.postharvbio.2021.111808 ↗
31. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
32. new-workspace zyqyt (2024, January 10). Storm Drain Model Dataset. Available online: https://universe.roboflow.com/new-workspace-zyqyt/storm-drain-model.
33. rootdataset (2024, January 10). Pipe-Root Dataset. Available online: https://universe.roboflow.com/rootdataset/pipe_root.
34. Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., and Wang, C. (2023). Rethinking mobile block for efficient neural models. arXiv.10.1109/ICCV51070.2023.00134 ↗
35. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
36. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
37. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17–24). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗