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Combining Deep Learning and JSEG Cuda Segmentation Algorithm for Electrical Components Recognition

机译:深度学习与JSEG Cuda分割算法相结合的电气元件识别

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A segmentation and recognition system for thermographic images of electric power distribution network using Artificial Intelligence is proposed in this article. The infrared thermography is usually used to proceed inspections in electrical power distribution lines, assisted by a human operator, which is usually responsible for operating all the equipment, selecting the hottest spots in the image (corresponding to the places needing maintenance), making reports and calling the technical team, which will do the repairs. The proposed automatic diagnosis system aims to replace the manual inspection operation using image processing algorithms. A method of segmentation for thermal images known as JSEG is implemented and tested and a Convolution Neural Network is responsible to recognize the segmented elements. The results show the feasibility of the algorithm, and the monitoring system.
机译:本文提出了一种基于人工智能的配电网热像图像分割识别系统。红外热像仪通常用于在配电线路上进行检查,并由操作员协助,该操作员通常负责操作所有设备,选择图像中最热的点(对应于需要维护的地方),进行报告和致电技术团队进行维修。提出的自动诊断系统旨在取代使用图像处理算法的人工检查操作。实现并测试了一种称为JSEG的热图像分割方法,卷积神经网络负责识别分割后的元素。实验结果表明了该算法和监控系统的可行性。

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