SC-Lite: An Efficient Lightweight Model for Real-Time X-Ray Security Check

Efficient and rapid analysis of X-ray images is crucial for detecting contraband, including weapons and explosives, at transportation hubs. While existing models like YOLOv8 offer high performance, they often fall short in real-time scenarios due to slow processing speeds. This study proposes SC-Lite, an improved model based on YOLOv8, designed to improve real-time detection of prohibited items in environments with limited computational power, such as embedded systems. We propose a novel acceleration module, the CSPNet Faster Convolution Network Module (C2F_FM), which reduces computational redundancy and optimizes memory usage by focusing standard convolutions on key channels, thereby enhancing efficiency. Furthermore, the Adaptation-BiFPN module improves multi-scale feature fusion with dynamic, content-aware weighting, increasing both the sensitivity and accuracy of detections. Additionally, the LAMP pruning strategy is utilized to optimize the model for deployment in resource-limited environments, significantly reducing the complexity of its architecture. After extensive experimental validation, SC-Lite outperforms all mainstream models in terms of real-time performance and detection accuracy, and the model outperforms existing methods in all metrics. The model achieved reductions of 72%, 62%, and 70% in parameters, computation, and model size, respectively. On the LSIray dataset, SC-Lite boosted the mean average precision (mAP) by 0.3% to 94.3% and enhanced the frame rate from 87 FPS to 136 FPS, an improvement of 49 FPS. For the OPIXray dataset, SC-Lite increased the mAP by 1.5% to 94.8%, with the frame rate rising from 344 FPS to 400 FPS, an improvement of 56 FPS. This study provides an innovative solution to improve the real-time performance and accuracy of security inspection systems.