Delving Into Cluttered Prohibited Item Detection for Security Inspection System

Hao Wang, Tong Jia, Bowen Ma, Dongyue Chen, Shizhuo Deng

Abstract: Prohibited item detection in X-Ray baggage images can efficiently prevent the social security and stability. With the development of deep learning, applying specific methods in computer vision tasks to prohibited item detection has shown promising perspectives, and the resulted intelligent security inspection system can effectively address the limitations of human inspection. Despite several deep learning based methods have been proposed to flourish this researching field, there are still two issues which have not been fully explored. First, most of them suffer from the shortage of dataset and collection of massive and well-annotated samples is extremely laborious and costly. Second, the designation of backbone modules for current prohibited item detection methods shares similar idea with the ones in object detection. While little consideration has been paid to the properties of prohibited item, where most of them are over-lapped and cluttered. To overcome limitations, this paper proposes a cluttered prohibited item detection method for security inspection system. Specifically, our method first generates synthetic X-Ray images through cut-and-paste strategy from the training samples in each training mini-batch, where the strategy effectively and efficiently augments the dataset and quality of the synthetic samples can be guaranteed. Then, a high-order dilated convolution module is developed for enriching the representation ability of the feature, further promoting the localization ability for over-lapped and cluttered prohibited items. Experiments show that our proposed method can be well generalized to various datasets, and achieve clear improvement over state-of-the-art methods.