Micro-cracks Detection of Solar Cells Based on Few Shot Samples with Multi-loss

Abstract

Aiming at the problem of micro-cracks detection of photovoltaic modules in industrial production line, in order to reduce labor cost, improve detection efficiency and quickly adapt to the micro-cracks detection of new products with the support of a few number of samples, a micro-cracks detection algorithm of solar cells based on few shot samples with multi-loss is proposed. Firstly, in order to enrich the semantic information extracted by convolutional neural network, Transformer’s multi-head attention mechanism is introduced to alleviate the impact of the distribution difference of each batch of products on crack detection, and promote the model to focus on crack information from diversified products. Secondly, the strategy of combining multiple loss functions to constrain the model training is used to optimize feature extraction. On the basis of direct classification loss, the triplet loss is used to shorten the feature distance between cracked samples. In addition, the implicit classification loss is designed to adapt to the characteristics of type differences between the two types of cells with or without cracks, and fully learn the diversity of historical component data. This algorithm can use a small number of samples to quickly extract the features of new components and detect micro-crack defects of new products accurately. The experimental results on the actual industrial production data sets show that the recall of the algorithm can be improved by 10 percentage points compared with other baseline models. This algorithm can effectively alleviate the problem of scarce samples with hidden cracks and greatly reduce the cost of frequent data labeling and model training for each batch of new products.

Keywords

• |solar cells|micro-cracks detection|few shot samples|deep learning|feature extraction