AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection
Mingyuan Li Tong Jia Hao Wang Bowen Ma Shuyang Lin Da Cai Dongyue Chen
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.