农机化研究2025，Vol.47Issue(7)：65-71,7.DOI:10.13427/j.issn.1003-188X.2025.07.009
基于改进YOLOv5的苹果轻量化检测算法
Lightweight Apple Detection Algorithm Based on Improved YOLOv5
摘要
Abstract
A lightweight apple detection algorithm based on YOLOv5 was proposed to solve the problems of complex net-work structure and large parameter quantity in the detection algorithm of apple picking robots.Firstly,the YOLOv5 back-bone network was replaced with MobileNetv3.To reduce the computational complexity of the network,deep separable convolution was introduced into the feature fusion network.Then,attention mechanisms were introduced at key locations in the network to improve the algorithm's ability to extract different features of apples.Finally,CIoU was used as the Loss function of the improved network to improve the detection effect of the model.The test results showed that the detec-tion accuracy of the improved model was 91.5%,which was 2.35%and 3.07%higher than SSD and Faster R-CNN,respectively.Compared to YOLOv5s,the detection accuracy had been improved by 8.20%,and the model size was about one-third of YOLOv5s.关键词
苹果/检测算法/YOLOv5/轻量化/注意力机制Key words
apple/detection algorithm/YOLOv5/lightweight/attention mechanism分类
农业科技引用本文复制引用
王红君,刘紫宾,赵辉,岳有军..基于改进YOLOv5的苹果轻量化检测算法[J].农机化研究,2025,47(7):65-71,7.基金项目
天津市科技支撑计划项目(19YFZCSN00360,18YFZCNC01120) （19YFZCSN00360,18YFZCNC01120）
