Weakly-Supervised Semantic Feature Refinement Network for MMW Concealed Object Detection

Abstract

The concealed object detection in millimeter-wave human body images is a challenging task due to the noise and dim-small objects. Exploiting the spatial dependencies to mine the difference between the object and the noise is vital for the discrimination of objects. However, most approaches ignore the context around the object. In this paper, a concealed object detection framework based on structural context is proposed to suppress noise interference and refine localizable semantic features. The framework consists of two subnetworks, structural region-based multi-scale weakly supervised feature refinement and local context-based concealed object detection. The multi-scale weakly supervised feature refinement is constructed to learn position-aware semantics of objects of various sizes while suppressing background noises in structural regions. Specifically, a multi-scale pooling method is proposed to better localize objects of different sizes, and an object-activated region enhancement module is designed to strengthen object semantic representations and suppress the background interference. Moreover, an adaptive local context aggregation module is designed to integrate the local context around the bounding box in the concealed object detection, which improves the discrimination of the model for the dim-small objects. Experimental results on the AMMW and the PMMW datasets demonstrate that the proposed approach improves detection performance with lower false alarm rates.