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

GSA-YOLO框架提升X射线安检速度和准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-20 03:36

研究人员开发了GSA-YOLO，一个专为实时X射线安检设计的新型轻量级框架。该模型基于YOLOv8n，集成了结构化稀疏性和自适应知识蒸馏，以提高检测准确性和推理速度。GSA-YOLO整合了Group Lasso、稀疏结构选择和自适应知识蒸馏机制，以增强特征表示并减小模型尺寸。在HiXray和PIDray数据集上的评估表明，GSA-YOLO在降低计算成本的同时，实现了189.62 FPS的领先推理速度，并且与基线模型相比，mAP50:95得分也有所提高。 AI

影响该新框架提高了X射线安检的速度和准确性，有望增强威胁检测能力。

排序理由该集群包含一篇详细介绍新模型及其在特定数据集上性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Jiahao Kong · 2026-05-20 03:36

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…

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

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