Self-paced feature attention fusion network for concealed object detection in millimeter-wave image

The active millimeter-wave (AMMW) scanner has been widely used for inspecting human security in public places in recent years owing to its ability to detect all kinds of objects under the clothes and be harmless to the body. However, it is really challenging to detect all concealed objects automatically and accurately due to inherent imaging noise, unknown object kind, and uncertain position. Recently, many existing methods, especially deep learning-based, have achieved good performances on concealed object detection. These methods work well for detecting a few kinds of large objects, but fail to perform on dim and incomplete hard objects. To address this task, a concealed object detection model with self-paced feature attention fusion network (SPFAFN) is proposed in this paper. To be specific, the features with different scales are fused in a top-down manner to integrate details and global semantics to better detect small objects. During fusing multi-scale features, a hierarchical pyramid attention mechanism composed of channel and spatial attention is developed to perceive the object. Moreover, boosting self-paced learning is exploited to guide the model to learn hard samples that are difficultly detected. The proposed method is validated on two real-world datasets: an AMMW dataset and a publicly available passive millimeter-wave (PMMW) dataset. Experimental results demonstrate that the proposed approach is superior to the state-of-the-art methods, and achieves better performances on the two datasets with Average Precision (AP).