MC-CDPNet: Multi-Channel Correlated Detail Preserving Network for X-Ray-Based Baggage Screening

Automation in searching and making decisions about the threat and non-threat objects from the baggage X-ray images could ease the security inspection systems rather than rely on human choices. Although various object detection and threat object identification methods have been proposed, at the present time, there are still many open-ended problems in Automatic Threat Detection from Baggage X-ray images. This paper proposes a novel framework, Multi-Channel Correlated Detail Preserving Network (MC-CDPNet), for segmenting threat objects based on LinkNet. The Correlated Detail Preserving block in the proposed network represents the target with location and channel based attention on different color space feature maps. The additional pooling model in encoder section also improves the ability to extract the prominent features. The performance of the proposed MC-CDPNet is equivalently successful on segmenting threat objects from the state-of-the-art datasets, GDXray, and SIXray, in comparison to many other existing methods.