research-article

An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s

Authors:

Chaolong Zhang,

Benjun GuoAuthors Info & Claims

SSPS '22: Proceedings of the 4th International Symposium on Signal Processing Systems

Pages 7 - 13

https://doi.org/10.1145/3532342.3532344

Published: 29 June 2022 Publication History

Abstract

Waste sorting and recycling play a significant role in carbon neutrality, and the government has promoted waste sorting stations in various cities while the stations have limited efficiency due to the absence of intelligent surveillance systems to monitor and analyze the scene in waste stations, especially to detect the irregularly dropped garbage. To take the most advantage of these stations, this study proposes an improved YOLO (You Only Look Once) v5s detector named YOLOv5s-Garbage to monitor waste sorting stations in real-time. This study enhances its ability to detect garbage by introducing CBAM (Convolutional Block Attention Module) and using EIoU (Efficient Intersection over Union) to accelerate the convergence of the bonding box loss. According to experiments, the mAP of YOLOv5s-Garbage on the waste sorting dataset reaches 89.7%, which is 3.3% higher than the classical YOLOv5s. This study then combines the DeepSort tracking algorithm and re-filter process to filter the target garbage to distinguish the irregularly dropped garbage and normal one, which reduces the false alarm significantly.

References

[1]

Law H, Deng J. 2018. Cornernet:Detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV). 734–750. https://doi.org/10.1007/s11263-019-01204-1

[2]

Zhang S, Chi C, Yao Y, Lei Z, Li S Z. 2020. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. IEEE, Seattle, WA, USA, 9759-9768. https://doi.org/10.1109/CVPR42600.2020.00978

[3]

Ren S, He K, Girshick R, Sun J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031

Digital Library

[4]

Shafiee M J, Chywl B, Li F, Wong A. 2017. Fast YOLO: A fast you only look once system for real-time embedded object detection in video. arXiv preprint arXiv:1709.05943.

[5]

Redmon J, Farhadi A. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

[6]

Bochkovskiy A, Wang C Y, Liao H Y M. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

[7]

Zhong Z, Zheng L, Kang G, Li S, Yang Y. 2020. Random erasing data augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 13001-13008. https://doi.org/10.1609/aaai.v34i07.7000

[8]

Wang C Y, Liao H Y M, Wu Y H, Chen P Y, Hsieh J W, Yeh I H. 2020. CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops(CVPRW). IEEE, Seattle, WA, USA, 390-391. https://doi.org/10.1109/CVPRW50498.2020.00203

[9]

Lin T Y, Dollár P, Girshick R, He K, Hariharan B, and Belongie S. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR). IEEE, Honolulu, HI, USA, 2117-2125. https://doi.org/10.1109/CVPR.2017.106

[10]

Liu S, Qi L, Qin H, Shi J, Jia J. 2018. Path aggregation network for instance segmentation.In Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR).IEEE, Salt Lake City, UT, USA, 8759-8768. https://doi.org/10.1109/CVPR.2018.00913

[11]

Woo S, Park J, Lee J Y, Kweon I S. 2018. Cbam: Convolutional block attention module.In Proceedings of the European conference on computer vision (ECCV). 3-19. Springer, Cham. https://doi.org/10.1007/978-3-030-01234-2_1

Digital Library

[12]

Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR). IEEE, Salt Lake City, UT, USA, 7132-7141. https://doi.org/10.1109/CVPR.2018.00745

[13]

Li Yao-shun, LIU Li-zhi. 2021 A lightweight rebar detection network with an attentional mechanism.Journal of Computer Applications.

[14]

Li Yongshang, Ma Ronggui, ZHANG Meiyue. Improved YOLOv5s+DeepSORT surveillance video traffic statistics. Computer Engineering and Applications. https://kns.cnki.net/kcms/detail/11.2127.TP.20211012.1539.010.html

[15]

Zheng Z, Wang P, Liu W, Li J, Ye R, and Ren D. 2020, April. Distance-IoU loss: Faster and better learning for bounding box regression.In Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12993-13000. https://doi.org/10.1609/aaai.v34i07.6999

[16]

Zhang Y F, Ren W, Zhang Z, Jia Z, Wang L, Tan T. 2021. Focal and Efficient IOU Loss for Accurate Bounding Box Regression. arXiv preprint arXiv:2101.08158

[17]

Proença P F, Simões P. 2020. TACO: Trash Annotations in Context for Litter Detection. arXiv preprint arXiv:2003.06975

Cited By

Jalal SAhmed HAhmed M(2023)Design a Robust Real-Time Trash Detection System Using YOLOv5 Variants2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)10.1109/GlobConET56651.2023.10149899(1-6)Online publication date: 19-May-2023
https://doi.org/10.1109/GlobConET56651.2023.10149899
Qiu SLi YZhao HLi XYuan X(2022)Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5Sensors10.3390/s2221820622:21(8206)Online publication date: 26-Oct-2022
https://doi.org/10.3390/s22218206

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cover image ACM Other conferences

SSPS '22: Proceedings of the 4th International Symposium on Signal Processing Systems

March 2022

116 pages

ISBN:9781450396103

DOI:10.1145/3532342

Copyright © 2022 ACM.

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Publication History

Published: 29 June 2022

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the Sichuan Science and Technology Programs
NSFC

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SSPS 2022

SSPS 2022: 2022 4th International Symposium on Signal Processing Systems

March 25 - 27, 2022

Xi'an, China

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Cited By

Jalal SAhmed HAhmed M(2023)Design a Robust Real-Time Trash Detection System Using YOLOv5 Variants2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)10.1109/GlobConET56651.2023.10149899(1-6)Online publication date: 19-May-2023
https://doi.org/10.1109/GlobConET56651.2023.10149899
Qiu SLi YZhao HLi XYuan X(2022)Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5Sensors10.3390/s2221820622:21(8206)Online publication date: 26-Oct-2022
https://doi.org/10.3390/s22218206

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