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GSA-YOLO framework boosts X-ray security inspection speed and accuracy

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

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT This new framework offers improved speed and accuracy for X-ray security inspections, potentially enhancing threat detection capabilities.

RANK_REASON The cluster contains a research paper detailing a new model and its performance on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

GSA-YOLO framework boosts X-ray security inspection speed and accuracy

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Jiahao Kong ·

    GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection

    X-ray security inspection requires accurate real-time detection of prohibited items, but existing models often struggle to balance the challenges of severe occlusion, complex clutter, and strict speed requirements. To overcome these challenges, this paper proposes GSA-YOLO, a nov…