Object Detection in Millimeter Wave Images Based on BoT-YOLOX

Active millimeter wave (AMMW) images are characterized by high noise, artifacts, and small objects, which has always been challenges for concealed object detection. Therefore, a method is proposed for detecting objects in millimeter-wave images based on BoT-YOLOX. Firstly, Bottleneck Transformer (BoT) is introduced into the model backbone network to enhance feature extraction capability of the model. Then, multi-scale object detection layer are adjusted, and global attention mechanism (GAM) is integrated to improve detection ability of small objects. Finally, a post-processing method of multi-view weighted boxes fusion is proposed to integrate the detection results of different views to improve the robustness of the model. On the large-scale AMMW image data set, in comparison with the benchmark model (YOLOX), the model achieves a detection rate of 93.22% and a false detection rate of 4.46%, and AP is increased by about 6.8%. On public data set, in comparison with the existing methods, the mAP is increased by 4.07%. The experimental results show that the proposed method is more accurate in detecting small targets in AMMW image scenes.