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 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 self-collected AMMW dataset, which includes 54 000 images, compared with the baseline model (YOLOX), the model achieves a detection rate of 93.22% and a false detection rate of 4.46%, and AP is increased by 6.74 percentage points. On the public AMMW dataset, compared with mainstream methods, the mAP is increased by 4.07 percentage points. The experimental results show that the proposed method is more accurate in detecting small targets in AMMW image scenes.