Collaborative Knowledge Injection for Concealed Object Detection in MMW Human Inspection

Millimeter-wave body screening technology has gained significant attention at inspection sites due to its noncontact and safety. However, existing concealed object detection methods still face challenges in real-world security scenarios, especially the missed detection of dim-small objects posing security risks. The challenge stems primarily from insufficient discrimination of features between small concealed objects and backgrounds. To this end, we propose a collaborative knowledge injection detection network (CKID-Net). It injects the object semantic knowledge learned from an external object database into the concealed object detection model, which forces the model to push background representations apart from the object prior knowledge, and pull together concealed object representations and the prior knowledge, thereby improving the model's discrimination. Our method collaboratively learns representations of prior objects and objects to be detected via excavating their semantic relation. Experiments on active millimeter-wave (AMMW) and terahertz (THz) human datasets show that the CKID-Net outperforms state-of-the-art methods, especially on detection rate.