A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-02, Vol.24 (4), p.1158
• Main Authors: Gao, Qingji, Deng, Haozhi, Zhang, Gaowei
• Format: Article
• Language: English
• Subjects: Accuracy; Airports; Algorithms; attention mechanism; Classification; contraband detection; Deep learning; deformable convolution net; Methods; Neural networks; pigeon-inspired optimization; Public transportation; Quality control equipment; Telecommunication systems; X-rays; YOLOv8s
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24041158

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.