Contextual Style Coherence Network for X-Ray Prohibited Item Image Synthesis.

Abstract

Prohibited item detection in X-Ray baggage images plays a crucial role for preventing the social security and stability. Well annotated X-Ray prohibited item training samples show necessity in achieving high detection performance for X-Ray inspection system. While collection of massive samples is extremely laborious and costly, especially for those X-Ray images, which need professional inspection machine. Synthesizing X-Ray images through Threat Image Projection (TIP) is a promising solution to overcome the data insufficient limitation in prohibited item detection. However, TIP based methods rarely consider the contextual style coherence between the foreground prohibited items and background images, resulting in generating low realistic X-Ray security images. For improving image quality and diversity, we propose a Contextual Style Coherence Network for X-Ray Prohibited item Image Synthesis. Specifically, we first propose a style fusion module to guarantee the style coherence and consistency between the foreground prohibited items and background images. We transfer the threat image projection from image space to feature space, and an affine transformation matrix is applied to uniformly sample the location, ratio and scale of the prohibited items to improve the sample diversity. We further normalize the features of the foreground prohibited item by implementing the style transfer through Gram matrix. Then, a mask partial convolution is designed for inpainting the non-object regions of the foreground prohibited items to achieve a better style transition, especially for the boundary parts. The whole network follows the adversarial training pipeline in an unsupervised manner guided by the incorporation of adversarial loss and total variation regularization. We evaluate the synthetic images generated by our method from different evaluating metrics including image quality and object detection performance on various prohibited item detection datasets. The results verify that our method can effectively generate realistic X-Ray prohibited item images and improve the detection performance.