CFIPC‐YOLO for Concealed Object Detection in Millimetre Wave Images

ABSTRACT Human body security inspection is a critical task, and most existing methods rely on millimetre‐wave images to detect concealed objects. However, detecting small and dim objects remains a significant challenge due to their subtle visual characteristics. To address this issue, this study proposes an efficient model, CFIPC‐YOLO (contextual features integrated progressive convergence network based on the YOLOv8), which integrates local and contextual features into a progressively converging framework based on YOLO. Experimental results show that CFIPC‐YOLO achieves a 5.3% improvement in AP50 compared to the YOLOv8 baseline, while simultaneously reducing the number of parameters by 17%, confirming the model's superior performance.