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ACD-Net: An Abnormal Crew Detection Network for Complex Ship Scenarios.

Zhengbao Li1, Heng Zhang1, Ding Gao1

  • 1College of Ocean Science and Engineering, Shandong University of Science and Technology, No. 579 Qianwan Port Road, Huangdao District, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

This study introduces ACD-Net, an AI system for detecting abnormal crew behavior on ships. It achieves high accuracy in real-time detection and identification, enhancing maritime safety.

Keywords:
YOLOv5sabnormal behavior detectionface quality assessmentfacial imagesidentity recognition

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Area of Science:

  • Maritime Safety
  • Artificial Intelligence
  • Computer Vision

Background:

  • Abnormal crew behavior is a significant factor in ship accidents.
  • Current recognition algorithms struggle in complex, open ship environments.
  • Real-time detection and identification of abnormal behavior are crucial for safety.

Purpose of the Study:

  • To propose an advanced abnormal crew detection network (ACD-Net) for complex ship scenarios.
  • To enhance real-time detection and identification capabilities for abnormal crew behavior.
  • To improve the performance of algorithms in open and dynamic shipborne environments.

Main Methods:

  • Developed an improved YOLOv5s model (YOLO-TRCA) with transformer and CBAM for enhanced feature extraction.
  • Integrated a Crew Face Algorithm (CFA) using CenterFace, Filter, and ArcFace for real-time facial recognition.
  • Employed a two-stage approach for detecting and identifying abnormal crew members.

Main Results:

  • ACD-Net achieved 92.3% accuracy in abnormal behavior detection.
  • Achieved a 69.6% matching rate for identity recognition.
  • Processed data in under 39.5 ms per frame at 1080P resolution.

Conclusions:

  • ACD-Net significantly improves abnormal crew detection and identification in complex maritime settings.
  • The proposed methods enhance accuracy and reduce computational overhead.
  • The system offers a viable solution for real-time monitoring and safety enhancement on ships.