Data Augmentation Ensemble Module based on Natural Guidance for X-ray Prohibited Items Detection

Automatic prohibited items detection plays an important role in protecting public security. Till now, object detection powered by deep learning provides a promising solution to automatic security inspection. However, in the one hand, according to the imaging principle of X-ray images, texture information would be lost, and in the other hand, different prohibited items with the same material are easily confused for the similar imaging color, leading to poor detection performance. Thus, to improve the detection performance of the basic object detection models on prohibited items detection task, we first propose the Data Augmentation Ensemble Module (DAEM) based on Nature Guidance for more accurate prohibited items detection. Specifically, inspired by the fact that inspectors detect items based on the characteristics of prohibited items in nature, we introduce natural images as prior knowledge to build X-ray security image - natural image sample pairs for supervising the model training. Besides, we adopt data augmentation strategies to enhance the diversity of the X-ray images, and then we combine the predictions from different data augmentation methods by ensemble learning to yield more accurate results. We verify the DAEM's performance by plug it into three different object detection models, and the experiments demonstrate that our framework can significantly improve the performance compared with the SOTA method on the PIDray dataset.