Abstract

It is important to perform contraband inspections on items before they are taken into public places in order to ensure the safety of people and property. At present, the mainstream method of judging contraband is that security inspectors observe the X-ray image of objects and judge whether they belong to contraband. Unfortunately, contraband is often hidden under other normal objects. In a high-intensity working environment, security inspectors are very prone to missed detection and wrong detection. To this end, a detection framework based on computer vision technology is proposed, which is trained and improved on the basis of the current state-of-the-art YOLOX object detection network, and adopts strategies such as feature fusion, adding a double attention mechanism and classifying regression loss. Compared with the benchmark YOLOX-S model, the proposed method achieves a higher average accuracy, with an improvement of 5.0% on the public safety SIXray dataset, opening the way to large-scale automatic detection of contraband in public places.

© 2022 Optica Publishing Group

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