Timely and accurate detection of defects and contaminants in solar panels is critical for maintaining the efficiency and reliability of photovoltaic (PV) systems. While recent studies have applied deep learning to PV inspection, fair benchmarking across detector architectures and unbiased handling of class imbalance remain limited. This work presents a comprehensive benchmark of convolutional and transformer-based object detectors on UAV-captured RGB imagery of solar panels. It introduces a class-targeted augmentation strategy applied exclusively to the training split to mitigate imbalance without compromising evaluation integrity. Faster R-CNN with ResNet50 and MobileNetV3 backbones, RetinaNet with ResNet50, YOLOv5, YOLOv8, and Swin Transformer backbones integrated with Faster R-CNN (Tiny, Small, and Base variants) are evaluated. Performance is assessed using mean Average Precision (mAP) across multiple IoU thresholds, precision, recall, F1 score, and inference throughput to enable accuracy-throughput tradeoff analysis relevant to UAV deployment. Experimental results show that Faster R-CNN with a ResNet50 backbone achieves the highest localization accuracy, with mAP@0.5 of 0.893 and mAP@0.5:0.95 of 0.759, whereas the MobileNetV3 variant provides the best overall reliability balance, achieving recall of 0.745, F1-score of 0.809, and accuracy of 0.679 on the test set. The dataset and code will be released upon acceptance of the paper.

翻译：及时准确地检测太阳能电池板中的缺陷与污染物对于维持光伏系统的效率与可靠性至关重要。尽管近期研究已将深度学习应用于光伏检测，但在检测器架构间的公平基准测试以及对类别不平衡的无偏处理方面仍存在不足。本研究对基于卷积和Transformer的目标检测器在无人机采集的太阳能电池板RGB图像上进行了全面基准测试。提出了一种仅应用于训练集的类别针对性增强策略，以在不损害评估完整性的前提下缓解类别不平衡问题。评估了采用ResNet50和MobileNetV3骨干网络的Faster R-CNN、采用ResNet50骨干的RetinaNet、YOLOv5、YOLOv8，以及集成Faster R-CNN的Swin Transformer骨干网络（Tiny、Small和Base变体）。性能评估采用多IoU阈值下的平均精度均值、精确率、召回率、F1分数及推理吞吐量，以支持与无人机部署相关的精度-吞吐量权衡分析。实验结果表明，采用ResNet50骨干的Faster R-CNN实现了最高的定位精度，其mAP@0.5为0.893，mAP@0.5:0.95为0.759；而MobileNetV3变体提供了最佳的整体可靠性平衡，在测试集上实现了0.745的召回率、0.809的F1分数和0.679的准确率。数据集与代码将在论文录用后公开。