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

Lei Pang1, Hui Liu1, Yang Chen1

  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

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Summary

This study introduces a real-time weapon detection system using You Only Look Once (YOLO) algorithm on passive millimeter wave (PMMW) images. The YOLOv3-53 model achieved 95% accuracy at 36 FPS, proving effective for security screening.

Keywords:
YOLOv3concealed object detectiondeep learningneural networkpassive millimeter wavereal-time

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Area of Science:

  • Computer Vision
  • Security Technology
  • Machine Learning

Background:

  • Detecting concealed objects on the human body is critical for security.
  • Small metallic contraband requires rapid and accurate detection methods.
  • Passive millimeter wave (PMMW) imaging offers a non-ionizing approach for concealed object detection.

Purpose of the Study:

  • To propose and evaluate a real-time weapon detection method for PMMW imagery.
  • To apply the You Only Look Once (YOLO) algorithm, specifically YOLOv3, for detecting metallic weapons on the human body.
  • To assess the effectiveness of YOLOv3 models against other algorithms like Single Shot MultiBox Detector (SSD) using a small dataset.

Main Methods:

  • Utilized the You Only Look Once (YOLO) version 3 algorithm for object detection.
  • Trained and tested YOLOv3-13, YOLOv3-53, and SSD-VGG16 on a passive millimeter wave (PMMW) image dataset.
  • Focused on detecting small metallic weapons concealed under clothing.

Main Results:

  • The YOLOv3-53 model demonstrated a detection speed of 36 frames per second (FPS).
  • Achieved a mean average precision (mAP) of 95% on a GPU-1080Ti computer.
  • YOLOv3-53 outperformed other tested models in terms of accuracy, speed, and computational resources.

Conclusions:

  • The YOLOv3-53 model is highly effective and feasible for real-time weapon contraband detection in PMMW images.
  • The proposed method is suitable for security applications, even with limited training data.
  • Real-time PMMW-based weapon detection using YOLOv3 offers a promising security solution.