用于电力线检测图像分析和处理的自动深度学习系统:架构和设计问题

IF 1.9 Q4 ENERGY & FUELS Global Energy Interconnection Pub Date : 2023-10-01 DOI:10.1016/j.gloei.2023.10.008
Daoxing Li , Xiaohui Wang , Jie Zhang , Zhixiang Ji
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引用次数: 0

摘要

无人机在输电线路检测中的应用规模不断增长,导致对无人机检测图像处理的需求相应增加。由于其在计算机视觉方面的优异性能,深度学习已被应用于无人机检测图像处理任务,如电力线识别和绝缘子缺陷检测。尽管性能优异,但基于深度学习的电力无人机检测图像处理模型面临着应用范围小、需要不断重新训练和优化、研发成本高等问题;D由于深度学习的黑匣子和场景数据驱动特性,造成了金钱和时间成本。在本研究中,针对上述问题,提出了一种用于电力无人机检测图像分析和处理的自动化深度学习系统。该系统设计基于可概括性、可扩展性和自动化这三个关键设计原则。回顾了与这些设计原则密切相关的预训练模型、微调(下游任务自适应)和自动机器学习。此外,还提出了一种用于电力无人机检测图像分析和处理的自动化深度学习系统架构。构建了原型系统,并对绝缘子自爆和鸟巢识别两项电力无人机检测图像分析处理任务进行了实验。使用原型系统构建的模型对绝缘体自爆和鸟巢识别的mAP分别达到91.36%和86.13%。这表明系统设计理念是合理的,系统架构是可行的
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Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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