Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images

TL;DR

This paper introduces a novel approach to address extreme output imbalance in defect segmentation of photovoltaic module cells, improving accuracy across multiple architectures and datasets by harmonizing training and inference.

Contribution

It proposes an explicit measure for output imbalance, a distribution-based loss adaptable to various imbalances, and a compound loss with adaptive hyperparameters for consistent segmentation performance.

Findings

Outperforms existing methods on four datasets

Effective across four deep learning architectures

Handles extreme class imbalance in defect segmentation

Abstract

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.