Prohibited Item Detection in Airport X-Ray Security Images via Attention Mechanism Based CNN
Automation of security inspections is crucial for improving the efficiency and reducing security risks. In this paper, we focus on automatically recognizing and localizing prohibited items in airport X-ray security images. A top-down attention mechanism is applied to enhance a CNN classifier to additionally locate the prohibited items. We introduce a high-level semantic feedback loop to map the targets semantic signal to the input X-ray image space for generating task-specic attention maps. And the attention maps indicate the location and general outline of prohibited items in the input images. Furthermore, to obtain more accurate location information, we combine the lateral inhibition and contrastive attention to suppress noise and non-target interference in attention maps. The experiments on the GDX-ray image dataset have demonstrated the efficiency and stability of the proposed scheme in both single target detection and multi-target detection.
Prohibited item Detection Attention CNN
Maoshu Xu Haigang Zhang Jinfeng Yang
Tianjin Key Lab for Advanced Signal Processing,Civil Aviation University of China,Tianjin,China
国际会议
广州
英文
429-439
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）