X-Ray Image Contraband Detection Based on Improved YOLOv8s

The variable size of contraband in X-ray images and mutual occlusion are the main factors for the low detection accuracy of small model target detection methods, in order to improve the accuracy of contraband detection under the restricted model parameters, an improved small YOLOv8SP contraband detection network is proposed. Aiming at the problem of different sizes of contraband and the difficulty of identifying small targets, a multi-size spatial pyramid pooling module is designed to realize multi-scale feature extraction by using a dense connection method. For the leakage detection problem caused by mutual occlusion of contraband, a parallel attention module is designed to improve the feature extraction ability of occluded objects. A large number of experiments prove that YOLOv8SP achieves 94.27% detection accuracy on the SIXray dataset at a very small scale, which is 2.13 percentage points higher than the original network, and the detection speed is 115 frames per second. It also has obvious advantages in terms of accuracy and computation speed compared with similar networks, which proves the effectiveness of the designed algorithm. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.