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%.