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Automatic solar panel recognition and defect detection using infrared imaging

机译：利用红外成像自动识别和检测太阳能电池板

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Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to "count" the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.
机译：太阳能电池板的无故障运行对于现代商业太阳能发电厂至关重要。为了实现更高的发电效率和更长的面板寿命，需要一种简单而可靠的面板评估方法。通过使用红外热成像，可以检测异常，而不必合并昂贵的电气检测电路。在本文中，我们提出了一种太阳能电池板缺陷检测系统，该系统可自动执行检查过程，并减轻了大型太阳能发电场中手动面板检查的需要。首先，通过安装在移动小车上的红外热像仪收集每个太阳能电池板阵列的红外视频序列，然后将其移动到太阳能发电场中的各个阵列中。图像处理算法可实时从背景中分割太阳能电池板，仅将阵列的高度（指定为阵列中电池板的行数）作为先验信息，以帮助进行分割过程。为了“计数”任何给定阵列中的面板数，使用光流建立帧与框之间的关联。一旦在视野中识别出面板，便会立即检测并标记单个面板中的局部异常，例如热点和裂缝。收集了整个阵列的数据后，将使用DBSCAN群集检测热面板。在包含超过12,000个太阳能电池板的实际测试数据中，超过98％的太阳能电池板被识别并正确计数，系统识别出所有类型的缺陷中的92％。

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