Learning Surface-awareness Network for X-Ray Prohibited Item Detection
Ying Ping Shen, Wei Li, Zhaoquan Yuan, Xiao Ying Wu · 2023
X-ray image security detection is a crucial method used to identify various types of prohibited items in luggage. However, the unique characteristics of X-ray imaging can result in the loss of intricate surface details, leading to subpar detection of prohibited items within X-ray images. In this paper, a Surface-aware Prohibited Item X-ray Detection Network (SPIXDet) is proposed to address this issue, which incorporates two key components: the Boundary Aggregation Module (BAM) and the Global Cross-Feature Downsampling layer (GCFD). The BAM module effectively mines image edge information while minimizing the number of parameters involved. Meanwhile, the GCFD module is introduced to mitigate chaotic interference caused by undifferentiated boundary boosting. The surface-aware capability of the model can be enhanced through the BAM and GCFD module. Furthermore, the Focal-SIoU loss function is introduced to increase positioning accuracy and optimize the model training process. To validate the effectiveness of our model, extensive experiments are conducted on the SIXray100 dataset, and the results demonstrate the advantages of SPIXDet compared to other X-ray prohibited item detection methods.