• Amol Salunkhe
• Dr. B. D. Jitkar

Nowadays due to increase in the awareness of the green and clean energy, the solar cells production increases in lightning speed. So, there is an increasing need for automating the solar cell panel defect detection instead of manual screening. So solar cell surface detection is becoming one of the crucial research areas for providing the reliable and optimistic manufacturing process. Recently the various deep learning frameworks are used for detecting the defects including YOLOv5, Faster R-CNN, and YOLOv6. These techniques are used for addressing the various challenges including accessing the background details, inconsistencies and variable defects. This review paper examines various techniques in the detection of solar cell defect, features extraction techniques, training classifiers, and architectural modifications. It also highlights the various challenges including data imbalance, real time data, use of explainable AI for detecting and interpreting the defects form the solar cells.

Solar Cell Defect Detection, Computer Vision, Deep Learning, YOLOv5, Faster R-CNN, Cross Stage Partial Network, YOLOv6, Deformable Convolution, Industrial Automation, Photovoltaic Inspection.

"Solar Cell Surface Defect Detection Deep Learning: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.c218-c221, October-2025, Available :http://www.jetir.org/papers/JETIR2510229.pdf

"Solar Cell Surface Defect Detection Deep Learning: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppc218-c221, October-2025, Available at : http://www.jetir.org/papers/JETIR2510229.pdf