Exploring X-Ray Prohibited Item Detection From Long-Tailed Learning Perspective

Boying Wang, Fuwei Zhang, Xiangfei Fang, Ruyi Ji, Renshuai Tao, Yaming Cao, Bo Liu, Jing Liu

Abstract: Existing X-ray prohibited item detection methods primarily focus on boosting the detection performance of uniformly distributed items. However, in the real-world scenarios, various prohibited items exhibit the long-tailed distribution, thus posing the huge challenge to the detection task. To support this study, we introduce LTXRay, a dedicated X-ray benchmark that better assesses long-tailed prohibited item detection. LTXRay consists of 18,718 images from 12 common classes with an imbalance factor of 280.35. Meanwhile, we propose a novel Memory-Guided Learning Network(MGLNet) to develop baseline methods on LTXRay, which enhance the within-class diversity for the tail classes and consequentially improves long-tailed object detection. Specifically, we first introduce a frequency-based feature refinement module to extract discriminative contextual representations, then store the various instance features in the memory bank and dynamically generate the sample according to the historical features. Extensive experiments have been performed on the LTXRay to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed method can consistently improve the performance of baseline methods.

External IDs:dblp:journals/tifs/WangZFJTCLL25