Intelligent detection and recognition of high voltage tower bird's nest based on improved neural network

Qunyuan Li; Daili Liang; Nan Chen; Wentong Zhang; Lixuan Ye; Yumeng Wang; Zhijian Shao; Yiming Peng; Jiayi Huo

doi:10.1117/12.2675372

25 May 2023 Intelligent detection and recognition of high voltage tower bird's nest based on improved neural network

Qunyuan Li, Daili Liang, Nan Chen, Wentong Zhang, Lixuan Ye, Yumeng Wang, Zhijian Shao, Yiming Peng, Jiayi Huo

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361G (2023) https://doi.org/10.1117/12.2675372
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

The YOLOv5 used in this paper is a single-segment target detection algorithm that improves the speed and precision performance based on YOLOv4. The original anchor frame of YOLOv5 is difficult to cope with the uneven distribution of detection targets in different training sets. In this paper, the dynamic anchor frame mechanism is used to improve YOLOv5. After improvement, the accuracy of the model was 93.5 %, increased by 4.2 %, the recall rate was 92.31 %, increased by 3.64 %. Through the actual data detection, the YOLOv5s model with dynamic anchor frame mechanism can better detect the bird's nest on the high voltage tower.

Citation Download Citation

Qunyuan Li, Daili Liang, Nan Chen, Wentong Zhang, Lixuan Ye, Yumeng Wang, Zhijian Shao, Yiming Peng, and Jiayi Huo "Intelligent detection and recognition of high voltage tower bird's nest based on improved neural network", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361G (25 May 2023); https://doi.org/10.1117/12.2675372

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KEYWORDS

Education and training

Target detection

Detection and tracking algorithms

Neural networks

Data modeling

Sampling rates

Image processing

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