Manual inspections for solar panel systems are a tedious, costly, and error-prone task, making it desirable for Unmanned Aerial Vehicle (UAV) based monitoring. Though deep learning models have excellent fault detection capabilities, almost all methods either are too large and heavy for edge computing devices or involve biased estimation of accuracy due to ineffective learning techniques. We propose a new solar panel fault detection model called HybridSolarNet. It integrates EfficientNet-B0 with Convolutional Block Attention Module (CBAM). We implemented it on the Kaggle Solar Panel Images competition dataset with a tight split-before-augmentation protocol. It avoids leakage in accuracy estimation. We introduced focal loss and cosine annealing. Ablation analysis validates that accuracy boosts due to added benefits from CBAM (+1.53%) and that there are benefits from recognition of classes with imbalanced samples via focal loss. Overall average accuracy on 5-fold stratified cross-validation experiments on the given competition dataset topped 92.37% +/- 0.41 and an F1-score of 0.9226 +/- 0.39 compared to baselines like VGG19, requiring merely 16.3 MB storage, i.e., 32 times less. Its inference speed measured at 54.9 FPS with GPU support makes it a successful candidate for real-time UAV implementation. Moreover, visualization obtained from Grad-CAM illustrates that HybridSolarNet focuses on actual locations instead of irrelevant ones.

翻译：太阳能电池板系统的人工检测是一项繁琐、昂贵且易出错的任务，因此基于无人机（UAV）的监测成为理想选择。尽管深度学习模型具备出色的故障检测能力，但几乎所有方法要么对于边缘计算设备而言过于庞大和沉重，要么由于无效的学习技术导致准确率估计存在偏差。我们提出了一种名为HybridSolarNet的新型太阳能电池板故障检测模型。该模型将EfficientNet-B0与卷积块注意力模块（CBAM）相结合。我们在Kaggle太阳能电池板图像竞赛数据集上实施了严格的“增强前分割”协议，避免了准确率估计中的泄露问题。我们引入了焦点损失和余弦退火策略。消融分析验证了CBAM带来的准确率提升（+1.53%），并证实了焦点损失通过识别样本不平衡的类别所带来的益处。在给定竞赛数据集上进行的5折分层交叉验证实验中，整体平均准确率达到92.37% ± 0.41，F1分数为0.9226 ± 0.39，相较于VGG19等基线模型，仅需16.3 MB存储空间，即减少了32倍。在GPU支持下，其推理速度测得为54.9 FPS，使其成为实时无人机部署的成功候选方案。此外，从Grad-CAM获得的可视化结果表明，HybridSolarNet能够聚焦于实际相关区域而非无关位置。