GPU Cuda JS']JSEG Segmentation Algorithm associated with Deep Learning Classifier for Electrical Network Images Identification

被引:6
|
作者
Fambrini, Francisco [1 ]
Iano, Yuzo [2 ]
Caetano, Diogo Gara [2 ]
Rodriguez, Abel A. D. [2 ]
Moya, Clodoaldo [2 ]
Carrara, Eduardo [2 ]
Arthur, Rangel [2 ]
Cabello, Frank C. [2 ]
Von Zubem, Joao [2 ]
Del Val Cura, Luis Mariano [3 ]
Destro-Filho, Joao Batista [4 ]
Campos, Jose Roberto [1 ]
Saito, Jose Hiroki [1 ,3 ]
机构
[1] Univ Fed Sao Carlos, BR-13565905 Sao Carlos, Brazil
[2] Univ Estadual Campinas, UNICAMP, BR-13083970 Campinas, Brazil
[3] UNIFACCAMP, Campo Limpo Paulista, Brazil
[4] Univ Fed Uberlandia, BR-38400902 Uberlandia, MG, Brazil
关键词
Thermography; Image Processing; Deep Learning; CUDA [!text type='JS']JS[!/text]EG Segmentation; Preventive Maintenance; Infrared Photo; INFRARED THERMOGRAPHY;
D O I
10.1016/j.procs.2018.07.290
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An automatic recognizer system based in Artificial Intelligence for thermographic images of the electric power distribution network is proposed in this article. The infrared thermography is usually used to conduct 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. An old method of segmentation for thermal images known as JSEG is implemented and tested and a Deep Learning Neural Network is responsible to recognize the segmented elements. A comparison between the exclusive Deep Learning based image recognition with the same method preceded by the JSEG segmentation algorithm is done in this article, showing better performance with this previous segmentation of the thermographic images. (C) 2018 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:557 / 565
页数:9
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