AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection
<|reference_start|>AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection: Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test.<|reference_end|>
@article{li2024ao-detr:,
    title={AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection},
    author={Mingyuan Li, Tong Jia, Hao Wang, Bowen Ma, Shuyang Lin, Da Cai, and
  Dongyue Chen},
    journal={arXiv preprint arXiv:2403.04309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04309},
    primaryClass={cs.CV cs.AI}
}