Study on terrain classification methods of mobile robots for photovoltaic modules cleaning

被引：0
|
作者：
Li C. [1 ]
Liu S. [1 ]
Gong J. [1 ]
机构：
[1] College of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 03期
关键词：
Bag-of-visual-word model; Classification of terrain; K-means clustering; Mobile robots; PV modules;
D O I：
10.19912/j.0254-0096.tynxb.2020-0551
中图分类号：
学科分类号：
摘要：
Considering the classification of uneven terrain for photovoltaic power stations during photovoltaic panel cleaning by mobile robots, a classification method based on W-MC-MS was then proposed to optimize the Bag-of-visual-word Model to classify the terrain images. First of all, the SIFT features are extracted from the actual terrain images of the photovoltaic power stations; then, the K-means clustering algorithm is adopted to calculate these features and generate the initial code dictionary of terrain images; afterwards the W-MC-MS criterion is adopted for optimization so as to reduce the size of codebook dictionary and improve the performance of visual terrain classification. The experiment considered the average classification accuracy and the cost of classification time regarding the terrain images as the evaluation standards, compared the classification performance of the codebook dictionary before and after optimization, and finally verified the validity of the optimization method in terrain classification. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：210 / 215
页数：5
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