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<p>To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.</p>
</abstract>

To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.

Research on the Application of Intelligent Recognition Technology in the Prediction of Violation Behaviour at Electricity Work Sites

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This...

Research on the Application of Intelligent Recognition Technology in the Prediction of Violation Behaviour at Electricity Work Sites

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 11 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0365

Słowa kluczowe
CNN, Face recognition, Tri-FPN, HOG feature extraction, Violation behavior recognition, Power operation site

© 2024 Chunhui Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Application of Intelligent Recognition Technology in the Prediction of Violation Behaviour at Electricity Work Sites

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 11 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0365

Słowa kluczoweCNN, Face recognition, Tri-FPN, HOG feature extraction, Violation behavior recognition, Power operation site

© 2024 Chunhui Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
CNN, Face recognition, Tri-FPN, HOG feature extraction, Violation behavior recognition, Power operation site