AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

• URL: http://arxiv.org/abs/2403.04309v1
• Date: Thu, 7 Mar 2024 08:30:17 GMT
• Title: AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection
• Authors: Mingyuan Li, Tong Jia, Hao Wang, Bowen Ma, Shuyang Lin, Da Cai, and Dongyue Chen
• Abstract summary: We propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. To address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forwardly scheme.
• Score: 6.603436370737025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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