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Manual inspection, still widely used, struggles with limited accuracy and scalability in high-throughput scenarios. Although existing object detection models perform well on natural images, they often fail to generalize to X-ray imagery due to occlusion, clutter, and the presence of small or non-metallic items. To address these challenges, we propose iX-Det, a novel X-ray threat detection network tailored for transportation security applications. iX-Det integrates two key components: a distraction-aware and path-augmented feature pyramid network (DAPA-FPN), and a C3 attention mechanism (C3-AM). DAPA-FPN enhances multiscale object representation while reducing clutter common in X-ray scans. Meanwhile, C3-AM introduces spatially adaptive attention into the backbone to improve feature extraction for restricted item detection. Extensive experiments on public X-ray datasets demonstrate that iX-Det significantly outperforms existing methods in both detection accuracy and inference speed. These results highlight its potential for deployment in real-world threat screening systems within intelligent transportation infrastructure.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf126","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T13:03:20Z","timestamp":1763125400000},"page":"125-140","source":"Crossref","is-referenced-by-count":3,"title":["iX-Det: An intelligent X-ray threat detection network for security inspection"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1313-826X","authenticated-orcid":false,"given":"Qianxiang","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Macau University of Science and Technology , Macau 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