Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm

• Main Authors: Lei Pang, Hui Liu, Yang Chen, Jungang Miao
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Sensors
• Subjects: concealed object detection; passive millimeter wave; deep learning; yolov3; neural network; real-time
• Online Access: https://www.mdpi.com/1424-8220/20/6/1678

The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.