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This project explores the necessity of few-shot detection for rare photovoltaic (PV) defects, highlighting the limitations of supervised learning due to the scarcity of examples, such as only 134 star-crack samples. It introduces OURS_RARE_PV, a model utilizing a Faster R-CNN with a ResNet50-FPN backbone, which learns base defects before fine-tuning with limited samples. The effectiveness of this approach is demonstrated through scenarios achieving up to 81.4% mean average precision (mAP) for...