Appl Opt. 2022 Jul 20;61(21):6297-6310. doi: 10.1364/AO.461627.
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.
在将物品带入公共场所之前，对其进行违禁品检查以确保人员和财产安全非常重要。目前，判断违禁品的主流方法是安全检查人员观察物品的 X 射线图像，判断它们是否属于违禁品。不幸的是，违禁品经常隐藏在其他正常物品之下。在高强度的工作环境中，安全检查人员非常容易出现漏检和误检。为此，提出了一种基于计算机视觉技术的检测框架，该框架在当前最先进的 YOLOX 目标检测网络的基础上进行了训练和改进，并采用了特征融合、添加双注意力机制和分类回归损失等策略。与基准 YOLOX-S 模型相比，所提出的方法实现了更高的平均准确率，在公共安全 SIXray 数据集上提高了 5.0%，为公共场所的大规模自动违禁品检测开辟了道路。
