绝缘子自爆检测是无人机电力输电线路巡检的一项重要内容，准确、快速、自动寻找绝缘子自爆区域，能大量节省无人机巡检图像数据处理工作量，提高巡检的精度和效率。本文针对目前绝缘子自爆区域检测存在样本少、目标小且精度不高的问题，提出了一种融合深度学习的无人机巡检绝缘子自爆区域检测方法。该方法用大量绝缘子样本训练深度学习目标识别模型，在识别出绝缘子区域内利用计算机视觉方法对自爆区域进行检测。本文方法综合了深度学习检测复杂目标，以及计算机视觉无需大量样本且能够检测小目标的优点。实验表明：本文算法对缺陷的检测精度能够达到84.8%，对于绝缘子自爆检测具有积极的意义和应用价值。
Insulator self-exploding detection is an important part of UAV inspection. Accurate, rapid and automatic searching for insulator self-exploding areas can greatly save the workload of UAV inspection data processing and improve inspection accuracy and efficiency. Aiming at the problem of low sample size, small target and low precision in the current insulator self-exploding detection, this paper proposes a deep learning self-exploding detection method for UAV inspection insulators. The method uses a large number of collected insulator samples to train the deep learning detection model, and then uses the computer vision method to detect the self-exploding region in the detected insulator. The method of this paper synthesizes the advantages of deep learning in detecting complex targets and the fact that computer vision does not require a large number of samples and can detect small targets. Experiments show that the detection accuracy of this algorithm can reach 84.8%. It has positive significance and application value for insulator self-exploding detection.
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