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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Logistics driver dangerous behavior recognition based on edge features

Hou Guijie1
Wang Cheng1
Xia Yuan2
Du Lin2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
2. Jiangyin Yiyuan-Jiangnan University Joint Laboratory of Industrial Intelligent Maintenance, Wuxi Jiangsu 214400, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0251

Abstract

Accurately recognizing dangerous behaviors such as talking on the phone among logistics drivers is an important part of achieving production safety. To solve the problems of complex scene and high similarity of drivers' arm movements in industrial field, an algorithm EF-GCN(Edge Feature Graph Convolutional Networks) , which combines edge features, is proposed to identify dangerous behaviors of logistics drivers. First, a spatial perception module based on adaptive graph convolution is proposed, taking into account joint points far away from the center of mass during human movement, and designing a weight allocation algorithm to improve recognition accuracy. Secondly, spatial temporal edge attention module is designed, and edge convolution is added after spatial temporal averaging to improve the shortcomings of insufficient edge feature extraction by the model; Meanwhile, the Separable Convolution Block (SC Block) is introduced to replace the standard convolution in the backbone network and reduce the amount of model parameters. Finally, a Similar Feature Recognition Network(SF-RN) is constructed to distinguish similar arm behaviors such as making phone calls and smoking, and strengthen ability of the algorithm to recognize similar behaviors. Experimental results show that the EF-GCN algorithm improves the recognition accuracy by 10.4% compared with the traditional spatial temporal graph convolution network and 3.2% compared with the baseline model. It can accurately recognize the dangerous behaviors of logistics drivers, verifying the effectiveness of the algorithm.

Foundation Support

近地面探测技术重点实验室基金资助项目(6142414220203)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0251
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-06] Accepted Paper

Cite This Article

侯贵捷, 王呈, 夏源, 等. 联合边缘特征的物流驾驶员危险行为识别 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.06.0251. (Hou Guijie, Wang Cheng, Xia Yuan, et al. Logistics driver dangerous behavior recognition based on edge features [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.06.0251. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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