A novel deep learning framework for detecting seafarer’s unsafe behavior-Reference-Cited by-同舟云学术

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A novel deep learning framework for detecting seafarer’s unsafe behavior

Published:2023-02-06 Issue:12 Volume:15 Page:1299-1324
ISSN:1943-9962
Container-title:Journal of Transportation Safety & Security
language:en
Short-container-title:Journal of Transportation Safety & Security

Author:

Ding Haifeng¹,Weng Jinxian¹²^ORCID,Han Bing²³

Affiliation:

1. College of Transport and Communications, Shanghai Maritime University, Shanghai, P.R. China

2. National Engineering Research Center of Ship & Shipping Control System, Shanghai, P.R. China

3. Shanghai Ship and Shipping Research Institute Co., Ltd, Shanghai, P.R. China

Publisher

Informa UK Limited

Subject

Safety Research,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/19439962.2023.2169801

Reference31 articles.

1. Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

2. Multidimensional simulation in team training for safety and security in maritime transportation

3. Video-based detection infrastructure enhancement for automated ship recognition and behavior analysis;Chen X.;Journal of Advanced Transportation,,2020

4. Visual ship tracking via a hybrid kernelized correlation filter and anomaly cleansing framework

5. R-fcn: Object detection via region-based fully convolutional networks;Dai J.;In Advances in Neural Information Processing Systems,,2016

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1. Seafarer competency analysis: Data-driven model in restricted waters using Bayesian networks;Ocean Engineering;2024-11

2. GD-YOLO: An improved convolutional neural network architecture for real-time detection of smoking and phone use behaviors;Digital Signal Processing;2024-08

3. Exploring the influence of seafarers’ individual characteristics on the perceived risk in Maritime emergencies: A simulator study;Journal of Transportation Safety & Security;2024-03-27

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