BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection

Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns.