Jing Bing, Duan Pianzhang, Chen Lu, Du Yanhui
School of Information and Network Security, People's Public Security University of China, Beijing 102206, China.
School of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China.
Sensors (Basel). 2023 Oct 18;23(20):8555. doi: 10.3390/s23208555.
Using X-ray imaging in security inspections is common for the detection of objects. X-ray security images have strong texture and RGB features as well as the characteristics of background clutter and object overlap, which makes X-ray imaging very different from other real-world imaging methods. To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE). We used the Soft-WIoU NMS method to solve the problem of object overlap. To better extract features, the attention mechanism CBAM was added to the backbone. According to the results of several experiments on the SIXray dataset, our EM-YOLOv7 method can better complete prohibited-item-detection tasks during security inspection with detection accuracy that is 4% and 0.9% higher than that of YOLOv5 and YOLOv7, respectively, and other SOTA models.
在安全检查中使用X射线成像来检测物体很常见。X射线安全图像具有很强的纹理和RGB特征，以及背景杂波和物体重叠的特点，这使得X射线成像与其他现实世界成像方法有很大不同。为了更好地检测具有这些特征的安全X射线图像中的违禁物品，我们提出了EM-YOLOv7，它由边缘特征提取器（EFE）和材质特征提取器（MFE）组成。我们使用Soft-WIoU NMS方法来解决物体重叠问题。为了更好地提取特征，在主干网络中添加了注意力机制CBAM。根据在SIXray数据集上的几次实验结果，我们的EM-YOLOv7方法在安全检查期间能够更好地完成违禁物品检测任务，其检测准确率分别比YOLOv5和YOLOv7以及其他最先进模型高4%和0.9%。
