基于SuNet的公共交通安检违禁品的检测

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中图分类号：TP391.4 文献标志码：ADOI：10.13338/j.issn.1674-649x.2025.02.006

Prohibited items detection in public transportation security inspection based on SuNet

ZHANG Huanhuan，LIU Pengcheng，JIANG Meng，WANG Yuxin （School of Electronics and Information，Xi'an Polytechnic University，Xi'an 710o48，China）

Abstract In the scenario of security inspection in public transportation，the overlapping of pro

hibited and non-prohibited items made it difficult for existing models to effectively identify obscured prohibited item categories.To address this issue，a prohibited item detection model based on SuNet was proposed in this paper. Firstly，an augmented attention localization feature pyramid network（AALFPN） was designed to enhance the semantic information of prohibited items and fuse the localization information and semantic information of prohibited items to guide the model in accurately locating obscured prohibited items，enhancing the feature contour of prohibited items.Secondly，a dense attention mechanism （DAM） was introduced to effectively identify and extract obscured prohibited items.Finally，the SmoothL1 Loss loss function was introduced to address the problem of loss of prohibited item category information during regression. To verify SuNet's ability to effectively identify obscured prohibited item categories，this study conducted experiments on the PIDray dataset. To assess SuNet's generalization on other prohibited item datasets，this study conducted experiments on the CLCXray dataset. Experimental results show that on the PIDray dataset，compared to the RoIAttn model，the SuNet improves by 2.9%，4.4% and 3.3% on the AP@0.5：0.95，AP@0.5 and AP @0.75 metrics，respectively. On the CLCXray data set，compared to the RoIAttn model，the SuNet improves by 1.4%，1.4% and 0.4% on the AP @0.5：0.95，AP@0.5 and AP@0.75 metrics，respectively. The experimental results demonstrate that SuNet not only effectively identifies obscured prohibited item categories but also exhibits good generalization performance on other prohibited item datasets，providing an effective solution for prohibited item detection in the public transportation security inspection scenario.

Keywordsprohibited item detection; SuNet;augmented attention localization feature pyramid network （AALFPN） ;dense attention mechanism（DAM） ;SmoothLl Loss

0引言

近年来，随着我国公共交通运输业的快速发展，违禁品（违禁品包括枪、榔头等物品）安全检查在火车站、汽车站、机场、地铁等公共场所是一项必不可少的工作，对于维护公共场所安全至关重要。（剩余14859字）