Multi-Dimensional Information Fusion You Only Look Once Network for Suspicious Object Detection in Millimeter Wave Images

Abstract

Millimeter wave (MMW) imaging systems have been widely used for security screening in public places due to their advantages of being able to detect a variety of suspicious objects, non-contact operation, and harmlessness to the human body. In this study, we propose an innovative, multi-dimensional information fusion YOLO network that can aggregate and capture multimodal information to cope with the challenges of low resolution and susceptibility to noise in MMW images. In particular, an MMW data information aggregation module is developed to adaptively synthesize a novel type of MMW image, which simultaneously contains pixel, depth, phase, and diverse signal-to-noise information to overcome the limitations of current MMW images containing consistent pixel information in all three channels. Furthermore, this module is capable of differentiable data enhancements to take into account adverse noise conditions in real application scenarios. In order to fully acquire the augmented contextual information mentioned above, we propose an asymptotic path aggregation network and combine it with YOLOv8. The proposed method is able to adaptively and bidirectionally fuse deep and shallow features while avoiding semantic gaps. In addition, a multi-view, multi-parameter mapping technique is designed to enhance the detection ability. The experiments on the measured MMW datasets validate the improvement in object detection using the proposed model.