Classify and Localize Threat Items in X-Ray Imagery With Multiple Attention Mechanism and High-Resolution and High-Semantic Features
Ruiyang Xia, Guoquan Li, Zhengwen Huang, Lingyun Wen, Yu Pang · IEEE Transactions on Instrumentation and Measurement · 2021
No doubt incorporating deep learning in security public transportation is an attractive and challenging research direction. In this article, we incorporate multiple attention mechanisms with high-resolution and high-semantic features into neural networks to further classify and localize threat items automatically. We propose our efficient model located in different network layers. To be specific, our model is involved in three modules called the spatial attention module (SAM), the channel attention module (CAM), and the high-resolution and high-semantic module (HRHSM). For shallow and deep layers, we adapt SAM and CAM to mine the long-range spatial and channel information of the network, respectively. With regard to the last few layers with different sizes, we leverage HRHSM to fuse their features so that the output contains high-resolution and high-semantic features. Due to the generalizability, our model can be broadly applied to many commonly used networks. Experimental results on the SIXray dataset that is consisted of six classes of threat item in X-ray images, CIFAR-10, and CIFAR-100 datasets demonstrate that our modules can improve the performance of networks in image classification and object localization without ground-truth bounding boxes.