融合深度学习的无人机巡检绝缘子自爆检测研究

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...

Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning

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.