<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research basis. A Faster-R-CNN network structure model is constructed to realize end-to-end target detection by combining RPN and Fast-R-CNN network structure. To further improve the detection accuracy, the BAM algorithm is introduced to enhance the Faster-R-CNN, to realize the accurate detection of hidden dangers in transmission and OPGW lines. This paper also compares the performance of the traditional and improved algorithms, and explores the practical application effect of the constructed model in depth. The experimental results show that the enhanced Faster-R-CNN algorithm significantly improves the correctness of observation in the sky and land regions, with an average accuracy mean value of about 26%, especially when observing field villages, factories, playgrounds, urban areas and swimming pools. Therefore, the improved algorithm proposed in this study effectively enhances the detection capability and accuracy of hidden safety hazards in transmission and OPGW lines.</p>
</abstract>

This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research basis. A Faster-R-CNN network structure model is constructed to realize end-to-end target detection by combining RPN and Fast-R-CNN network structure. To further improve the detection accuracy, the BAM algorithm is introduced to enhance the Faster-R-CNN, to realize the accurate detection of hidden dangers in transmission and OPGW lines. This paper also compares the performance of the traditional and improved algorithms, and explores the practical application effect of the constructed model in depth. The experimental results show that the enhanced Faster-R-CNN algorithm significantly improves the correctness of observation in the sky and land regions, with an average accuracy mean value of about 26%, especially when observing field villages, factories, playgrounds, urban areas and swimming pools. Therefore, the improved algorithm proposed in this study effectively enhances the detection capability and accuracy of hidden safety hazards in transmission and OPGW lines.

Research on key technology of transmission and OPGW line hidden danger prediction based on neural network

Applied Mathematics and Nonlinear Sciences

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

This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research...

Research on key technology of transmission and OPGW line hidden danger prediction based on neural network

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 10 sty 2024

Przyjęty: 15 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0459

Słowa kluczowe
Transmission and OPGW lines, Faster-R-CNN model, BAM algorithm, Neural network, Hidden danger prediction

© 2024 Xin Wang et al., published by Sciendo

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

Research on key technology of transmission and OPGW line hidden danger prediction based on neural network

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 10 sty 2024

Przyjęty: 15 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0459

Słowa kluczoweTransmission and OPGW lines, Faster-R-CNN model, BAM algorithm, Neural network, Hidden danger prediction

© 2024 Xin Wang et al., published by Sciendo

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

Słowa kluczowe
Transmission and OPGW lines, Faster-R-CNN model, BAM algorithm, Neural network, Hidden danger prediction