基于无人机图像的配电和杆塔鸟巢检测方法

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-19 DOI:10.1049/cps2.12073
Yue Meng, Yu Song, Yuquan Chen, Xin Zhang, Mei Wu, Biao Du
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引用次数: 0

摘要

作者提出了一种新型物体检测算法,用于识别中压电力线航空图像中的鸟巢,这对确保电网的安全运行至关重要。该算法利用改进的 Swin Transformer 作为快速 R-CNN 的主要特征提取网络,并进一步增强了通道注意和改进的二进制自注意机制,以提高特征表示能力。我们在一个新构建的包含鸟巢的中压输电线路图像数据集上对所提出的算法进行了评估,并对其进行了注释和分类。实验结果表明,与传统算法相比,所提出的算法在识别鸟巢方面达到了令人满意的准确性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A swin transformer based bird nest detection approach with unmanned aerial vehicle images for power distribution and pole towers

The authors propose a novel object detection algorithm for identifying bird nests in medium voltage power line aerial images, which is crucial for ensuring the safe operation of the power grid. The algorithm utilises an improved Swin Transformer as the main feature extraction network of Fast R-CNN, further enhanced with a channel attention and modified binary self-attention mechanism to improve the feature representation ability. The proposed algorithm is evaluated on a newly constructed image dataset of medium voltage transmission lines containing bird nests, which are annotated and classified. Experimental results show that the proposed algorithm achieves satisfied accuracy and robustness in recognising bird nests compared to traditional algorithms.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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