SAM-PIE: SAM-Enabled Photovoltaic-Module Image Enhancement for Fault Inspection and Analysis Using ResNet50 and CNNs

Different models have been developed for segmentation tasks, each with its uniqueness. Recently, the Segment Anything Model (SAM) was added to the pool of these models with expectations of addressing their weaknesses. SAM, although trained on a huge dataset for segmentation of anything, particularly images of natural source, produces suboptimal results when applied to segmentation of photovoltaic module image due to difference in semantic between photovoltaic module and natural images. In spite of the current suboptimal performance of SAM in segmentation of photovoltaic module images, it demonstrates detection and identification of thermal anomalies in photovoltaic module images that majorly contribute to power production loss. The implication of this is that, the task, the model, and the data corresponding to SAM are applicable to photovoltaic module image diagnosis. In this paper, we propose SAM-enabled photovoltaic-module image enhancement (SAM PIE) for fault inspection and analysis using ResNet50 and CNNs. SAM-PIE combines the strength of SAM for enhancement of the fault inspection and analysis procedure, for optimal performance of the proposed method. Experiments were performed on three thermal anomaly image datasets of photovoltaic modules to validate the performance of SAM-PIE for the classification tasks. The results obtained validates the potential capability of SAM-PIE to perform photovoltaic module image classification. The dataset is publicly and freely available for scientific community use at https://doi.org/10.17632/5ssmfpgrpc.1

@article{Bello2024,
  title     = {SAM-PIE: SAM-Enabled Photovoltaic-Module Image Enhancement for Fault Inspection and Analysis Using ResNet50 and CNNs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Rotimi-Williams Bello and Pius A. Owolawi and Etienne A. van Wyk and Chunling Du},
  doi       = {10.14569/IJACSA.2024.0151132},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151132}
}