DAI J, LI L X, ZHAO J W, et al.Research on garbage grasping object detection algorithm based on improved YOLOv5s[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(3):49-58.

doi:10.16186/j.cnki.1673-9787.2023110027

Received:2025/02/13

Revised:2025/06/31

Published:2026/04/28

Research on garbage grasping object detection algorithm based on improved YOLOv5s

Dai Jun1, Li Lixiang1, Zhao Junwei1, Yuan Xingqi1, Liu Gang1, Cheng Xiaoqi2

1.School of Mechanical and Power Engineering， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.School of Mechatronic Engineering and Automation， Foshan University， Foshan  528225， Guangdong， China

Abstract: Objectives Intelligent waste classification is a future development trend. To improve the accuracy and speed of waste classification and grasping while reducing labor costs， a waste detection method based on an improved YOLOv5s is proposed.  Methods First， the backbone network of YOLOv5s is replaced with MobileNet， which has fewer parameters and faster computation， instead of the original CSPDarkNet53. The CBAM attention mechanism is introduced into the detection head of the network. Second， a waste classification dataset is constructed and augmented. The improved YOLOv5s （Improved YOLOv5s） model is trained on this dataset and compared with the original YOLOv5s model through experiments. Finally， the improved network model is deployed on a waste grabbing robot platform （including control system， vision system， grasping system， and mobile base） for grasping tests.  Results Experimental results show that， compared with the original YOLOv5s model， the Improved YOLOv5s model has 3.63×10⁶ parameters， which is only 51.63% of the original model. The training speed is increased by 60% （5 min/epoch）， and the detection speed is increased by 20% （33 fps）. The mean average precision （mAP） is 0.44. Grasping experiments indicate that the robot performs well on regularly shaped， lightweight waste： the success rate for waste paper balls is 95%， and for plastic items it is 85%.  Conclusions The proposed object detection algorithm achieves a good balance between detection accuracy and algorithmic complexity. It is more lightweight than the original YOLOv5s， making it suitable for deployment on mobile and embedded platforms. It improves the real time performance and success rate of the waste grabbing robot， and provides practical value for the development of intelligent waste classification and sanitation.

Key words:object detection;garbage grasping;lightweight neural network;waste classification;intelligence