Abstract
Partial shading and hot spots may cause power loss and sometimes irreversible damage of photovoltaic (PV) modules. In order to evaluate the power generation of PV modules, it is necessary to calculate the area of shading and hot spots. The perspective distortion makes region closer to the camera have more pixels, which results in misestimate of shading area and hot spots. To solve this problem, a robust automatic method for removing projective distortion of photovoltaic modules from close shot images is proposed in this paper. Firstly, Images are converted to gray scale, and their edges are detected by Canny algorithm. Then, lines are detected by Hough transform, vanishing points are found by intersecting lines, and the line at infinity is specified by vanishing points. Next, the projective transformation matrix is decomposed into an affine transformation matrix and a simple projective transformation matrix, which has only two degrees of freedom. Affine rectification is derived from computing this simple projective transformation matrix, and finally, right angles are recovered by computing shear transformation matrix. The close shot visible and infrared images are collected for experiments. The results show that the proposed method performs better on rectification of PV modules from close shot images.
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Acknowledgement
This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB1500800), and by the National Natural Science Foundation of China (Grant No. 61773118, Grant No. 61703100, Grant No. 61973083, Grant No. 61802059), and by the Natural Science Foundation of Jiangsu (Grant No. BK20170692, Grant No. BK20180365), and by the Zhishan Young Scholar Program of Southeast University and the Fundamental Research Funds for the Central Universities (Grant No. 2242020R40119).
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Shen, Y., Chen, X., Zhang, J., Xie, L., Zhang, K., Wei, H. (2020). A Robust Automatic Method for Removing Projective Distortion of Photovoltaic Modules from Close Shot Images. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_59
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