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可见光与红外图像融合的瓷绝缘子钢帽温度提取与零值检测方法研究
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Abstract:
瓷绝缘子钢帽温度分布的获取是输配电线路绝缘子零值缺陷巡检的关键一环，针对复杂背景下红外图像中绝缘子钢帽温度自动获取困难的现状，融合可见光与红外图像各自优点，基于Faster-RCNN深度学习网络算法检测可见光图像中的绝缘子串，检测率达98.3%；基于绝缘子强弱边缘特征改良Sobel算子检测绝缘子边缘；利用绝缘子钢帽的矩形形状特征、渐变间距特征实现所有钢帽的提取与修正；研究同场景下绝缘子在可见光与红外图像的坐标转换关系，实现红外图像中钢帽提取。在对110 kV和220 kV绝缘子进行零值缺陷检测时，本方法可以准确提取绝缘子钢帽温度并发现零值绝缘子，具有良好的有效性和实用性。
Obtaining the temperature distribution of porcelain insulator steel cap is a key link in the patrol inspection of zero-value defects of transmission and distribution line insulators. It is difficult to obtain the temperature of insulator steel cap automatically in infrared image under complex background. Combining the respective advantages of visible and infrared images, insulator strings in visible light images are detected based on Faster-RCNN deep learning network algorithm, with a detection rate of 98.3%. The improved Sobel operator is used to detect the insulator edge based on the insulator strength edge feature. The rectangular shape feature and gradual spacing feature of insulator steel caps are used to extract and correct all steel caps. The coordinate conversion relationship between visible light and infrared image of insulator in the same scene is studied, and the steel cap extraction in infrared image is realized. When detecting zero-value defects of 110 kV and 220 kV insulators, this method can accurately extract the temperature of insulator steel cap and find zero-value insulators, which has good effectiveness and practicability.
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