湖南大学学报（自然科学版）2024，Vol.51Issue(6)：29-39,11.DOI:10.16339/j.cnki.hdxbzkb.2024264
基于改进YOLOv5的鱼眼图像目标检测算法
Object Detection Algorithm for Fish Eye Image Based on Improved YOLOv5
摘要
Abstract
The images collected by fish eye cameras in autonomous driving scenarios have severe distortion,complex scenes,drastic scale changes,and many small targets,which lead to low detection accuracy of traditional object detection models.Therefore,YOLOv5s-R,an improved fish eye image detection model based on YOLOv5s,is proposed.Firstly,to solve the problem of difficult recognition of minor targets,the RCMS(Random Crop Muti Scale)data augmentation method is proposed,which performs better than the optimal data augmentation method obtained from ablation experiments.Secondly,to improve the detection accuracy of the model,SA(Shuffle Attention)and LDH(Light Decouple Head)modules are added to the network header to enhance the model's feature extraction and recognition capabilities,suppress noise interference.Finally,an additional angle prediction branch is added to realize the rotating box object detection,a circular label is constructed to solve the PoA(Periodicity of Angular)problem,and the label is smoothed with the Gaussian function.The RIOU is proposed to optimize the loss function by adding an angle penalty term on the basis of CIOU,which improves the regression accuracy and speeds up the convergence of the model.The experimental results show that the proposed YOLOv5s-R model achieves good detection performance on the Woodscape dataset.Compared to the original YOLOv5s model,mAP@0.5 mAP@0.5 is 0.95 increased by 6.8%and 5.6%,respectively,reaching 82.6%and 49.5%.关键词
YOLOv5s/自动驾驶/鱼眼图像/旋转框目标检测Key words
YOLOv5/autonomous driving/fish eye images/rotating box object detection分类
计算机与自动化引用本文复制引用
韩彦峰,任奇,肖科..基于改进YOLOv5的鱼眼图像目标检测算法[J].湖南大学学报（自然科学版）,2024,51(6):29-39,11.基金项目
国家重点研发计划课题(2022YFB4702201),National Key R&D Program of China(2022YFB4702201) （2022YFB4702201）
