The continuous development of the photovoltaic (PV) industry has raised high requirements for the quality of monocrystalline of PV module cells. When learning to segment defect regions in PV module cell images, Tiny Hidden Cracks (THC) lead to extremely-imbalanced samples. The ratio of defect pixels to normal pixels can be as low as 1:2000. This extreme imbalance makes it difficult to segment the THC of PV module cells, which is also a challenge for semantic segmentation. To address the problem of segmenting defects on extremely-imbalanced THC data, the paper makes contributions from three aspects: (1) it proposes an explicit measure for output imbalance; (2) it generalizes a distribution-based loss that can handle different types of output imbalances; and (3) it introduces a compound loss with our adaptive hyperparameter selection algorithm that can keep the consistency of training and inference for harmonizing the output imbalance on extremelyimbalanced input data. The proposed method is evaluated on four widely-used deep learning architectures and four datasets with varying degrees of input imbalance. The experimental results show that the proposed method outperforms existing methods.

翻译：光伏产业的持续发展对光伏电池片单晶质量提出了高要求。在学习分割光伏电池片图像中的缺陷区域时，微小隐藏裂纹（THC）导致样本极度不平衡，缺陷像素与正常像素的比例可低至1:2000。这种极端失衡使得光伏电池片微小隐藏裂纹的分割变得困难，这也成为语义分割领域的一项挑战。为解决极度不平衡THC数据上的缺陷分割问题，本文从三个方面做出贡献：（1）提出一种输出失衡的显式度量方法；（2）推广了一种基于分布的损失函数，可处理不同类型的输出失衡；（3）引入一种复合损失函数，配合自适应超参数选择算法，可在极度不平衡输入数据上保持训练与推理的一致性，以协调输出失衡。该方法在四种广泛使用的深度学习架构和四个具有不同输入不平衡程度的数据集上进行了评估。实验结果表明，所提方法优于现有方法。