X-ray Image Prohibited Item Detection Algorithm Based on Improved PP-YOLO

In order to solve the problems of missing detection due to overlap and occlusion of contraband in X-ray images and low accuracy of small object detection, we propose a single-stage object detection framework based on PP-YOLO. Compared with the traditional prohibited item detection algorithm, it adds CBAM module on the basis of ResNet50 feature extraction network to enhance the feature extraction ability; For increasing the detail features of the detection layer, MSF module is introduced into FPN, which fuses the feature map with accurate position information in the lower layer and the feature map with strong semantic information in the higher layer; The partial convolution of backbone is improved to CompConv to accelerate the processing speed of the model, which compresses the network structure and improves the inference speed without losing performance. The results show that the mAP of the improved network for prohibited item detection is 94.67%, and the processing speed reaches 45 FPS, which means that the recognition accuracy and reasoning speed of this method have been improved to some extent.