Intelligent image recognition of unmanned aerial vehicle frontend based on neural networks

Shenli Wang; Yong Du; Hong Yin; Hongwei Hu; Chunxiao Zhou; Jun Wu

doi:10.1117/12.3031077

19 July 2024 Intelligent image recognition of unmanned aerial vehicle frontend based on neural networks

Shenli Wang, Yong Du, Hong Yin, Hongwei Hu, Chunxiao Zhou, Jun Wu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318111 (2024) https://doi.org/10.1117/12.3031077
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Airborne front-end intelligent image recognition is one of the key technologies for unmanned aerial vehicle inspection of transmission lines. It is fundamentally different from traditional image recognition and judgment methods. This article implements convolutional neural network mechanical learning based on artificial intelligence technology. Four models were established for equipment identification, tower detection, wire detection, and insulator string detection, which were trained and tested separately. Taking into account both training time and actual usage efficiency. This article recommends a basic training sample size of 20000 for the model. Comparing two different neural network structures, it is found that the model with a pooling value of 2 and a 1.28 million pixel input has higher recognition accuracy. The training results of different models indicate that for different equipment recognition models, the training amount required to achieve stable recognition accuracy is from small to large, in order of wires, towers, equipment, and insulator strings.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shenli Wang, Yong Du, Hong Yin, Hongwei Hu, Chunxiao Zhou, and Jun Wu "Intelligent image recognition of unmanned aerial vehicle frontend based on neural networks", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318111 (19 July 2024); https://doi.org/10.1117/12.3031077

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