内蒙古民族大学学报（自然科学版）2024，Vol.39Issue(4)：56-62,7.DOI:10.14045/j.cnki.15-1220.2024.04.011
基于改进YOLOv5的温室番茄果实检测算法
Greenhouse Tomato Fruit Detection Algorithm Based on Improved YOLOv5
摘要
Abstract
In order to realize the accurate and efficient operation of tomato picking robot in greenhouse environ-ment,a tomato fruit detection algorithm based on improved YOLOv5 deep learning network,SL-YOLOv5,was proposed.In the improved algorithm,the SPP(Spatial Pyramid Pooling)network structure is replaced by SimSPPF(Simplified Spatial Pyramid Pooling-Fast)to speed up the detection speed of tomato fruits,and the large separable kernel attention(LSKA)is introduced to improve the detection accuracy of tomato fruits.The algorithm was tested on a self-made greenhouse tomato fruit dataset,and the detection speed and accuracy of the network before and after the improvement were compared.The experimental results show that the improved SL-YOLOv5 algorithm has an accuracy rate of 0.963 for the recognition of ripe tomato fruits,0.951 for unripe tomato fruits,and 0.957 for the comprehensive recognition in the greenhouse environment,which is 2.4%higher than that of the original YOLOv5 algorithm,and the detection speed(FPS)is 6.75,which is 17.2%higher than that of the original YOLOv5 algorithm.The improved algorithm can detect tomato fruits in the greenhouse environment,classify ripe tomatoes and immature tomatoes,and effectively improve the speed and accuracy of tomato fruit detection in the greenhouse environment.关键词
YOLOv5/深度学习/农业机器人/目标检测/番茄Key words
YOLOv5/deep learning/agricultural robot/object detection/tomato分类
信息技术与安全科学引用本文复制引用
杨彩云,王磊,姚桂廷,张明宇,陈宇昂..基于改进YOLOv5的温室番茄果实检测算法[J].内蒙古民族大学学报（自然科学版）,2024,39(4):56-62,7.基金项目
安徽省自然科学基金项目(2208085MF169) （2208085MF169）
安徽省智能机器人信息融合与控制工程研究中心开放课题(IFCIR2024020) （IFCIR2024020）
