Concealed Object Detection for Millimeter-Wave Images With Normalized Accumulation Map

Automatically detecting dangerous concealed objects in the millimeter-wave images is important for imaging-aided security systems. In this paper, we proposed a normalized accumulation map-based training mechanism for concealed object detection network. The proposed normalized accumulation map, calculated as the average of binary masks representing the object location for each image, can reveal the positions of frequently-appeared concealed objects, which offers different weights for different locations when computing confidence loss. Experiments on a millimeter-wave security image dataset demonstrate the effectiveness of our proposed normalized accumulation map-based training mechanism. By introducing our training mechanism to YOLO-v2, the object detection network can achieve a 4.43% performance improvement in mean average precision.