基于改进YOLOv5s的水果目标检测研究

doi:10.16039/j.cnki.cn22-1249.2023.07.007

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摘要

为了准确识别果树上不同的水果目标，解决对果实识别精度不理想等问题，提出一种基于YOLOv5s的改进算法。首先，在回归框预测损失上，将EIoU损失函数应用到YOLOv5s上，使目标框与锚框的宽度和高度之差达到最小；其次，在主干网络上添加ECA-Net注意力机制，以更好地提取不同果实目标的特征。实验结果表明，YOLOv5s+ECA+EIoU loss的综合性能优势明显，与现有的几种网络算法对比，改进后的算法各项数据均优于其他算法，进而验证了改进的有效性，可以为机器人采摘时的目标检测研究提供必要的技术支持。

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	刘麒
	盛德庆
	孙万龙
	王影

关键词: 目标检测水果 YOLOv5s ECA-Net EIoU loss

Abstract:

An improved algorithm based on YOLOv5s was proposed in order to accurately identify different fruit targets on fruit trees and solve problems such as unsatisfactory fruit recognition accuracy. Firstly, the EIoU loss function is applied to YOLOv5s to minimize the difference between the width and height of the target frame and the anchor frame. Secondly, the ECA-Net attention mechanism was added to the backbone network to better extract the characteristics of different fruit targets. The experimental results show that YOLOv5s+ECA+EIoU loss has obvious comprehensive performance advantages. Compared with several existing network algorithms, various data of the improved algorithm is superior to other algorithms, thus verifying the effectiveness of the improved algorithm and providing necessary technical support for the research of object detection during robot picking.

Key words: target detection fruits YOLOv5s ECA-Net EIoU loss.

出版日期: 2023-07-25 发布日期: 2023-07-25 整期出版日期: 2023-07-25

ZTFLH:

TP391.41

引用本文:

刘麒, 盛德庆, 孙万龙, 王影.

基于改进YOLOv5s的水果目标检测研究 [J]. 吉林化工学院学报, 2023, 40(7): 34-41.
LIU Qi , SHENG Deqing , SUN Wanlong , WANG Ying. Research on Fruit Target Detection based on Improved YOLOv5s . Journal of Jilin Institute of Chemical Technology, 2023, 40(7): 34-41.

链接本文:

http://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2023.07.007 或 http://xuebao.jlict.edu.cn/CN/Y2023/V40/I7/34

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