GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection
Researchers have developed GSA-YOLO, a new lightweight framework designed for real-time X-ray security inspection. This model, based on YOLOv8n, incorporates structured sparsity and adaptive knowledge distillation to improve detection accuracy and inference speed. GSA-YOLO integrates Group Lasso, Sparse Structure Selection, and an Adaptive Knowledge Distillation mechanism to enhance feature representation and reduce model size. Evaluations on the HiXray and PIDray datasets show GSA-YOLO achieves a leading inference speed of 189.62 FPS with reduced computational cost, alongside improved mAP50:95 scores compared to the baseline. AI
IMPACT This new framework offers improved speed and accuracy for X-ray security inspections, potentially enhancing threat detection capabilities.