Detection of Microcracks and Dark Spots in Monocrystalline PERC Cells Using Photoluminescene Imaging and YOLO-based CNN with Spatial Pyramid Pooling

Two common defects encountered during manufacturing of crystalline silicon solar cells are microcrack and dark spot or dark region. The microcrack in particular is a major threat to module performance since it is responsible for most PV failures and other types of damage in the field. On the other hand, dark region in which one cell or part of the cell appears darker under UV illumination is mainly responsible for PV reduced efficiency, and eventually lost of performance. Therefore, one key challenge for solar cell manufacturers is to remove defective cells from further processing. Recently, few researchers have investigated deep learning as an alternative approach for defect detection in solar cell manufacturing. The results are quite encouraging. This paper evaluates the convolutional neural network based on heavy-weighted You Only Look Once (YOLO) version 4 or YOLOv4 and the tiny version of this algorithm referred here as Tiny-YOLOv4. Experimental results suggest that the multi-class YOLOv4 is the best model in term of mean average precision (mAP) and prediction time, averaging at 98.8% and 62.9 ms respectively. Meanwhile an improved Tiny-YOLOv4 with Spatial Pyramid Pooling scheme resulted in mAP of 91.0% and runtime of 28.2 ms. Even though the tiny-weighted YOLOv4 performs slightly lower compared to its heavy-weighted counterpart, however the runtime of the former is 2.2 order much faster than the later.