机电工程技术2025，Vol.54Issue(3)：97-101,126,6.DOI:10.3969/j.issn.1009-9492.2025.03.018
基于改进YOLOv5s的沙滩垃圾识别模型
Beach Litter Recognition Model Based on Improved YOLOv5s
摘要
Abstract
Garbage left on beach is increasing,but the efficiency of manual cleaning is low and the cost is high.At present,many of the vision-based garbage detection and sorting models generally have shortcomings in small target detection and feature extraction.In order to solve the above sorting problems,combined with deep convolutional neural network and object detection algorithm,the image classification algorithm based on deep learning is applied to beach garbage recognition.Based on the YOLOv5s model,the following improvement scheme is proposed:In order to solve the detection accuracy problem,the network is redesigned and P2 detection header is added to enhance the detection capability of small targets.In addition,in order to further improve the feature extraction capability of the network,CBAM attention mechanism is introduced and embedded between CNN convolutional layers.Experimental data show that under different illumination,angle,occlusion and distance,the accuracy of the proposed recognition model is 92%,which is 4%higher than that of the unimproved YOLOv5s,and the mAP value of the improved model is increased from 0.849 to 0.898.关键词
YOLOv5s/CBAM注意力机制/垃圾识别/沙滩垃圾/分类回收Key words
YOLOv5s/CBAM attention mechanism/garbage identification/beach litter/sorting and recycling分类
信息技术与安全科学引用本文复制引用
朱颖,夏靖邦,林康,卢颖裕,岑淑桢,郑壹,朱孝斌..基于改进YOLOv5s的沙滩垃圾识别模型[J].机电工程技术,2025,54(3):97-101,126,6.基金项目
2023年广东省科技创新战略专项资金(pdjh2023b0747) （pdjh2023b0747）
2022年广东省大学生创新训练项目(S202213656018) （S202213656018）
