Any defect in a solar cell will reduce the amount of electricity generated. Over time, these defects will result in hot spots. Currently, EL images are commonly utilized for defect detection during the production process of solar modules. However, manual visual inspection is primarily employed formodule defect assessment, leading to inconsistencies in standards and judgments.Our detection technology is based on the original and adaptable Yolo architecture. This technology incorporates innovative approaches like incremental learning and transfer learning to address the challenges associated with diverse flaw types and difficult data collection. Consequently, the accur
acy of defect identification has been significantly enhanced, and it can now achieve real-time detection of dozens of defects with an impressive accuracy rate of 99.8%. Furthermore, our solar panel IR thermal defects technology employs a novel object detection method specifically designed for detecting thermal in IR videos.

Our team's EL inspection software has been certified by KIWA in the Netherlands. In July 2020, "Msapiens Technology Co., Ltd." was established, and it secured investment from domestic venture capital firms. Presently, venture capital firms from the Netherlands and Germany are showing strong interest.
Four Technical Advantages:
(1) The largest number: it has more than 21 million EL images and has established the world's largest effective solar module EL image database.
(2) The most accurate detection: impressive accuracy rate of 99.8%. Other industries typically achieve accuracy rates ranging from 75% to 95%.
(3) The fastest detection rate: Our EL full-load detection speed reaches 18,000 pieces/hour. In comparison, other industries only manage 100 pieces/hour.
(4) Highest market share: successfully obtained 8 customers of the world 's top ten solar module manufacture