基于视觉传感的输电线路冰厚图像识别方法,Mobile Networks and Applications

为了获得高精度的输电线路冰厚图像识别结果，提出了一种基于视觉传感的输电线路冰厚图像识别方法。利用视觉传感器采集输电线路覆冰图像，将图像的非局部自相似信息作为正则化先验约束项，结合K-SVD算法建立图像去噪模型去除图像中的噪声。对图像中输电线路覆冰区域进行分割，并提取覆冰区域的边缘特征；基于区域像素计算冰厚，最终实现输电线路冰厚的图像识别。经过实验测试，经验证，采用该方法去噪图像的信噪比高于3.76 dB，能够有效提取输电线路覆冰的灰度特征，获得准确的覆冰厚度识别结果。计算结冰厚度与实际值偏差小于0.05 mm，结冰厚度识别精度高，具有较强的工程应用价值。

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In order to obtain high precision image identification results of transmission line ice thickness, an image identification method of transmission line ice thickness based on visual sensing was proposed. The ice-covered images of transmission lines are collected by visual sensors, and we take the non-local self-similarity information of the images as a regularized prior constraint term, which is combined with K-SVD algorithm to establish an image denoising model to remove the noise in the images. The ice-covered regions of transmission lines are segmented in the image and the edge features of ice-covered regions are extracted; we can calculate Ice thickness based on regional pixels, and finally realized the image identification of transmission line ice thickness. After experimental testing, it has been proven that the signal-to-noise ratio of the denoised image using the proposed method is higher than 3.76 dB, which can effectively extract the grayscale features of the icing on the transmission line and obtain accurate identification results of the icing thickness. The deviation between the calculated icing thickness and the actual value is less than 0.05 mm, and the accuracy of icing thickness identification is high, which has strong engineering application value.