An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s

Yi Zhan, Yuanping Xu, Chaolong Zhang, Zhijie Xu, Benjun Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationSSPS 2022
Subtitle of host publicationProceedings of the 4th International Symposium on Signal Processing Systems
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Electronic)9781450396103
Publication statusPublished - 25 Mar 2022
Event4th International Symposium on Signal Processing Systems - Virtual, Online, China
Duration: 25 Mar 202227 Mar 2022
Conference number: 4

Publication series

NameACM International Conference Proceeding Series
VolumePar F180473


Conference4th International Symposium on Signal Processing Systems
Abbreviated titleSSPS 2022
CityVirtual, Online


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