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GithubThe photovoltaic panel “appears to be fine,” but this does not necessarily indicate normal power generation efficiency. Local shading and dust accumulation often persist for extended periods yet remain difficult to detect. OpenMV enables photovoltaic operation and maintenance without full reliance on manual inspections.

During the operation of photovoltaic power plants, foreign substances such as dust accumulation, fallen leaves, and bird droppings on the surface of photovoltaic panels directly affect light absorption efficiency, resulting in reduced power generation; over the long term, these issues may even accelerate component aging and shorten equipment service life.
For large-scale photovoltaic arrays, manual inspection is not only inefficient but also unable to promptly detect localized anomalies. Leveraging SingTown’s OpenMV intelligent camera, long-term and stable visual monitoring of photovoltaic panel operational status can be achieved, enabling automatic identification and timely resolution of foreign object issues.
The camera can be securely mounted on a support structure to cover multiple photovoltaic module areas and continuously capture images of the photovoltaic panel surfaces. OpenMV performs region segmentation algorithms at the edge to delineate the effective operating areas of the photovoltaic panels and, by integrating foreign object feature recognition with status comparison analysis, automatically determines whether abnormal dust accumulation, leaf obstruction, or localized contamination is present.
By comparing the current image with the normal state, the system can identify abnormal areas affecting power generation efficiency and output the identification results. These results can directly trigger cleaning equipment, operation and maintenance systems, or alarm modules at the edge side to enable immediate handling upon detection, eliminating the need to upload large volumes of image data to the cloud.
Leveraging OpenMV’s edge computing capabilities and low-power design this solution is particularly suitable for long-term deployment in large-scale distributed photovoltaic scenarios.

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