Aiming at the problems of heavy workload and inaccuracy in the photographic management of aircraft maintenance, this paper proposes a method of using deep learning YOLOv4-tiny algorithm to perform photo comparison detection. A self-made data set is used to train the network model. In order to solve the problem of cotter nut and other background interference, the attention mechanism module is introduced to improve YOLOv4-tiny. Test results show that the precision (P) is improved by 5%, the recall (R) is improved by about 8%, and the mean average precision (mAP) is increased by 4.9%. It has excellent performance in photo recognition accuracy and positioning accuracy, and meets the requirements of accurate target recognition and comparison in photo management.