Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

HSV-1 strain H129 co-opts neuronal synaptic transmission machinery for its transsynaptic spread.

Nature neuroscience·2026

Same author

Correction: METTL3 accelerates staphylococcal protein A (SpA)-induced osteomyelitis progression by regulating m6A methylation-modified miR-320a.

Journal of orthopaedic surgery and research·2026

Same author

Correction: METTL3 accelerates staphylococcal protein A (SpA)-induced osteomyelitis progression by regulating m6A methylation-modified miR-320a.

Journal of orthopaedic surgery and research·2026

Same author

Efficacy and safety of doxycycline for severe Mycoplasma pneumoniae pneumonia in pediatric patients.

Clinical and experimental medicine·2025

Same author

TM9SF3 is a Golgi-resident ATG8-binding protein essential for Golgi-selective autophagy.

Developmental cell·2025

Same author

METTL3 accelerates staphylococcal protein A (SpA)-induced osteomyelitis progression by regulating m6A methylation-modified miR-320a.

Journal of orthopaedic surgery and research·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2025

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Published on: July 2, 2018

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

Summary

This summary is machine-generated.

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:

YOLOv5s abnormal behavior detection face quality assessment facial images identity recognition

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

Related Experiment Videos

Last Updated: Jun 6, 2025

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Published on: July 2, 2018

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

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.