Research on target recognition technology based on improved YOLOv5

Aiming at the problem of low detection accuracy in traditional UAV target recognition, an improved YOLOv5 target recognition method is proposed. The loss function of YOLOv5 is improved, and the CIoU loss function is used instead of the GIoU loss function used by YOLOv5 to optimize the training model. The accuracy of the algorithm is improved, and a more accurate identification of the target is realized. The experimental results show that the mAP value of the model trained on the aviation dataset NWPU VHR-10 by the improved YOLOv5 algorithm reaches 93.33%, which is 4% higher than the original YOLOv5 algorithm.