iX-Det: An intelligent X-ray threat detection network for security inspection

Abstract Automated threat detection in transported goods using X-ray screening plays a critical role in ensuring the safety and efficiency of modern transportation systems, including highway checkpoints, ports, and airports. 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.