Volume 40 Issue 2
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 22-29
CHEN Xinqiang, ZHENG Jinbiao, LING Jun, WANG Zichuang, WU Jianjun, YAN Ying. Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network[J]. Journal of Transport Information and Safety, 2022, 40(2): 22-29. doi: 10.3963/j.jssn.1674-4861.2022.02.003
Citation: CHEN Xinqiang, ZHENG Jinbiao, LING Jun, WANG Zichuang, WU Jianjun, YAN Ying. Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network[J]. Journal of Transport Information and Safety, 2022, 40(2): 22-29. doi: 10.3963/j.jssn.1674-4861.2022.02.003
shu

Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network

doi: 10.3963/j.jssn.1674-4861.2022.02.003
  • 1.

    Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

  • 2.

    Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

  • 3.

    School of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • Received Date: 2021-10-25
    Available Online: 2022-05-18
    Abstract
  • Identify abnormal behaviors of workers at ship working fields provides important information for intelligent shipping management and decision-making, which is conducive to promoting the development of smart ports and intelligent ships. To achieve this, an abnormal behavior recognition framework is proposed based on a novel asynchronous interaction aggregation (AIA) model. The proposed model implements the convolution operation on the maritime surveillance videos by using the YOLO algorithm. The convolution results are optimized using the feature pyramid to locate the human in each image. A method of joint learning of detection and an embedding model are then integrated to extract the spatial-temporal features of workers and objects. Furthermore, the proposed AIA model utilizes an interaction aggregation module that update multi-dimensional feature information in the feature pool to detect abnormal behaviors of workers at ship working fields. The results show that the average recognition accuracy of the proposed method is 91%, and the recognition accuracy is 85% at the working fields. For the ship bridge monitoring, the recognition accuracy of unsafe behaviors of crews can reach up to 97%. Based on its validity and reliability, the proposed framework can achieve good accuracy in a variety of ship working fields.

     

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