Prominent solution for solar panel defect detection using AI-based computer vision technology with IoT sensors in the solar panel manufacturing industry

The burgeoning demand for solar energy has propelled the largest solar panel manufacturer to the forefront of sustainable energy innovation. Recognizing the critical importance of quality assurance in maintaining industry leadership, the manufacturer has embarked on a transformative journey toward implementing automated defect detection systems. Leveraging the power of IoT sensors and computer vision, a new framework is proposed for defect detection in solar cells as well as solar panels. The proposed framework uses a camera to capture the images and an IoT sensor that is installed on the machine collects the physical parameters such as temperature, pressure, heat, and stress during the manufacturing process of solar cells. The images captured by the camera are collected as a dataset. The captured image in the dataset is preprocessed and trained by using the yolov7 (you only look once) algorithm which gives a benchmark result of 99.5% mean average precision (mAP), 94.6% precision, and 100% recall in correctly identifying the defect and classifying it. During the inspection, the physical parameters of the IoT sensors help in getting the cause of defects in the solar cell. The proposed work is compared with other existing approaches and provides a benchmark result by enhancing accuracy, efficiency, and reliability. Other computer vision algorithms can be used for defect detection and accuracy improvement in future results.