A low-complexity method for concealed object detection in active millimeter-wave images

Active millimeter wave imaging (AMWI) is an efficient way to detect dangerous objects concealed under clothes. However, because the images acquired by AMWI are often obscure and some of concealed objects are small in size, the automatic detection and localization of the objects remain as a challenging problem. Yao Di first employed convolutional neural networks (CNNs) and used a dense sliding window method to detect concealed objects. In this paper, the author presents two improvements over Yao's work: 1) Using contextual information to suppress interference and improve detection probability: 2) Using a two-step search method instead of exhaustive search to reduce the computational complexity. To reduce the computational complexity, the author first uses a CNN in vertical direction to filter the interference and obtain the vertical position of the concealed object, then uses another CNN to determine the horizontal position of the concealed object. To make use of big window containing contextual information, the author uses IoG (intersection-over-ground-truth) instead of IoU (Intersection-over-Union) to define positive and negative samples in training and testing process. Experimental results show that the proposed method will make the length of computational time reduced to about 30% of that of the exhaustive search while achieving better detection performance.