现代信息科技2025，Vol.9Issue(11)：33-37,5.DOI:10.19850/j.cnki.2096-4706.2025.11.007
基于YOLOv5的旋转边界框电容器目标检测
Object Detection of Rotated Bounding Boxes for Capacitors Detection Based on YOLOv5
摘要
Abstract
To solve the problem that classical YOLOv5 object detection algorithm can only achieve object localization with horizontal rectangular bounding boxes,this paper designs an object detection method of rotated bounding boxes for capacitors based on the YOLOv5s model.This method transforms the angle prediction problem from a regression problem to a classification problem by using circular smooth labels,and describes the loss function of angle prediction using binary cross-entropy loss.Additionally,the original training data is expanded through replication,rotation transformation,and stitching to improve the accuracy and generalization ability of the model.Experimental results on a real capacitors dataset show that the improved object detection algorithm of rotated bounding boxes for capacitors achieves an average accuracy of 83.5%.Compared with the original YOLOv5 model,the predicted object bounding boxes are more consistent with the actual rectangular contours of the capacitors.关键词
目标检测/旋转矩形框/深度学习/电容器元件Key words
Object Detection/rotated bounding box/Deep Learning/capacitor分类
信息技术与安全科学引用本文复制引用
张智浩,杨雪骏,沈谋全,胡记伟,柯云,李超超..基于YOLOv5的旋转边界框电容器目标检测[J].现代信息科技,2025,9(11):33-37,5.基金项目
国家自然科学基金项目(62303216) （62303216）
中国博士后基金(2023M731647) （2023M731647）
