TY - GEN
T1 - An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s
AU - Zhan, Yi
AU - Xu, Yuanping
AU - Zhang, Chaolong
AU - Xu, Zhijie
AU - Guo, Benjun
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/3/25
Y1 - 2022/3/25
N2 - 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.
AB - 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.
KW - Object detection
KW - Real-time monitoring
KW - Waste sorting
KW - YOLOv5s
UR - http://www.scopus.com/inward/record.url?scp=85133780802&partnerID=8YFLogxK
U2 - 10.1145/3532342.3532344
DO - 10.1145/3532342.3532344
M3 - Conference Proceeding
AN - SCOPUS:85133780802
T3 - ACM International Conference Proceeding Series
SP - 7
EP - 13
BT - SSPS 2022 - 2022 4th International Symposium on Signal Processing Systems
PB - Association for Computing Machinery
T2 - 4th International Symposium on Signal Processing Systems, SSPS 2022
Y2 - 25 March 2022 through 27 March 2022
ER -
