Aiming at the problems of safety monitoring of electric work vehicle in power operation scenes

a rotating target detection algorithm based on YOLOv5 is proposed. By detecting the rotation angle of mechanical arm of electric work vehicle

it can judge whether the working posture of electric work vehicle in the current operation scene is safe or not. The data annotation method of YOLOv5 is modified

and the circular smooth label method is adopted to transform the boundary regression problem of angle into a classification problem

so as to solve the sudden change of loss value caused by periodic change of angle. The experiment results show that the average detection accuracy of CSPDarkNetYOLOv5l model for the mechanical arm of electric work vehicle reaches 80.02%

which is better than GhostNetYOLOv5l

MobieleNetV3YOLOv5l and ShuffleNetV2YOLOv5l

and the prediction for the rotation angle of mechanical arm is also the closest to the real value. Key words：rotating target detection; angle prediction; circular smooth label; power operation