X-ray Detection of Prohibited Item Method Based on Dual Attention Mechanism

Abstract

Prohibited item detection plays a significant role in ensuring public safety, as the timely and accurate identification of prohibited items ensures the safety of lives and property. X-ray transmission imaging technology is commonly employed for prohibited item detection in public spaces, producing X-ray images of luggage to visualize their internal contents. However, challenges such as multiple object overlapping, varying angles, loss of details, and small targets in X-ray transmission imaging pose significant obstacles to prohibited item detection. Therefore, a dual attention mechanism network (DAMN) for X-ray prohibited item detection is proposed. The DAMN consists of three modules, i.e., spatial attention, channel attention, and dependency relationship optimization. A long-range dependency model is achieved by employing a dual attention mechanism with spatial and channel attention, effectively extracting feature information. Meanwhile, the dependency relationship module is integrated to address the shortcomings of traditional convolutional networks in terms of short-range correlations. We conducted experiments comparing the DAMN with several existing algorithms on datasets containing 12 categories of prohibited items, including firearms and knives. The results show that the DAMN has a good performance, particularly in scenarios involving small object detection, detail loss, and target overlap under complex conditions. Specifically, the detection average precision of the DAMN reaches 63.8%, with a segmentation average precision of 54.7%.

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“…To solve this problem, Wang et al [24] proposed an efficient background learning method, which includes mixed foreground and background learning, hierarchical balanced hard negative example sampler and prime background mining with voting. Li et al [25] proposed a model called Dual Attention Mechanism Network for X-ray prohibited item detection. The model consists of three key modules, including spatial attention, channel attention and dependency optimisation, to solve challenges such as multitarget overlap, angle changes, detail loss and small targets.…”