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GithubThe photovoltaic panel “appears normal,” but this does not necessarily indicate normal power generation efficiency. Localized 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 PV panels directly reduce light absorption efficiency, leading to decreased power generation; over time, 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 cameras, 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 fixedly deployed on a support structure to cover multiple photovoltaic module areas and continuously capture images of the photovoltaic panel surfaces. OpenMV performs region segmentation at the edge to delineate the effective operating areas of the photovoltaic panels and automatically detects anomalies such as abnormal dust accumulation, leaf obstruction, or localized contamination by combining foreign-object feature recognition with status comparison analysis.
By comparing the current image with a normal reference, the system identifies abnormal areas affecting power generation efficiency and outputs the identification results. These results can directly trigger on-device cleaning equipment, operation and maintenance systems, or alarm modules to enable immediate response 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 especially suitable for long-term deployment in large-scale, distributed photovoltaic scenarios.

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