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

针对自动驾驶场景下车载鱼眼相机采集到的图像存在畸变严重、场景复杂、尺度变化剧烈、小目标多以及传统的目标检测模型的检测精度不高的问题,提出了一种基于YOLOv5s改进的鱼眼图像检测模型YOLOv5s-R.首先,为解决小目标难识别的问题,提出随机裁剪多尺度训练的数据增强方法,该方法优于消融实验所得的最优数据增强方法.其次,为了提高模型的检测精度,在网络头部添加置换注意力机制与轻量化解耦头,增强模型对特征的提取能力与识别能力,并抑制噪声干扰.最后,模型额外增加角度预测项,实现旋转框目标检测.通过构建环形标签并用高斯函数对标签平滑,解决了旋转框角度的周期性问题;又对损失函数进行了优化,提出了RIOU,在CIOU的基础上增加角度惩罚项,提高了回归精度并加快了模型的收敛.实验结果表明,提出的YOLOv5s-R模型在WoodScape数据集上取得良好的检测效果,相比于原始的YOLOv5s模型,mAP@0.5、mAP@0.5∶0.95分别提升了6.8%、5.6%,达到82.6%、49.5%.

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%.