摘要
为了解决传统YOLOv5目标检测算法在检测小目标时存在检测精度不高和漏检的问题,提出一种基于改进YOLOv5的小目标检测方法。首先,为了使Anchor Box能更好地适应小目标,在K-means聚类过程中,使用IOU(Interp Over Union)替换原始使用的欧几里得距离公式,重新定义Anchor Box和Ground Truth之间的距离;其次,在空间金字塔池化(Spatial Pyarmid Pooling,SPP)上增加一个池化核大小为3×3的最大池化,提高对小目标的检测精度;最后,制作一个包含多种小型目标的数据集以验证算法性能。实验结果表明:改进YOLOv5算法的验证平均精度(mean Average Precision,mAP)达到76.92%,与经典YOLOv5算法相比提升了3.56个百分点,检测效果有所提升且能检测出漏检目标。
In order to solve the problems of low detection accuracy and missing detection in traditional YOLOv5 object detection algorithm,a small object detection method based on improved YOLOv5 was proposed.Firstly,to make anchor box better adapt to small targets,IOU(interp over union)is used to replace the Euclidean distance formula originally used in the K-means clus⁃tering process to redefine the distance between anchor box and ground truth.Secondly,a maximum pooling of 3×3 kernel size is added to spatial pyarmid pooling(SPP)to improve the detection accuracy of small targets.Finally,a data set containing a variety of small object is designed to verify the algorithm performance.Experimental results show that the mean average precision(mAP)of the improved YOLOv5 algorithm reaches 76.92%,which is 3.56 percentage points higher than that of the classical YOLOV5 algorithm.The detection performence is improved and missed object can be detected.
作者
王艺成
张国良
张自杰
WANG Yi-cheng;ZHANG Guo-liang;ZHANG Zi-jie(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Yibin 644000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Yibin 644000,China)
出处
《计算机与现代化》
2023年第5期100-105,共6页
Computer and Modernization
基金
四川省应用基础研究项目(2019YJO413)
四川轻化工大学基础研究项目(E10402733)。
