Towards Real-World X-Ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

Renshuai Tao, Yanlu Wei, Xiangjian Jiang, Hainan Li, Haotong Qin, Jiakai Wang, Yuqing Ma, Libo Zhang, Xianglong Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10923-10932

Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport security inspection and annotated by professional security inspectors. Besides, for accurate prohibited item detection, we further propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information and focusing on identifiable characteristics, especially when objects are overlapped with each other. Specifically, LIM, the elaborately designed flexible additional module, suppresses the noisy information flowing maximumly by the Bidirectional Propagation (BP) module and activates the most identifiable charismatic, boundary, from four directions by Boundary Activation (BA) module. We evaluate our method extensively on HiXray and OPIXray and the results demonstrate that it outperforms SOTA detection methods.