DEFECT DETECTION FOR AEROSPACE SOLAR CELLS BASED ON IMPROVED YOLOX-s ALGORITHM
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摘要: 针对航天太阳电池表面缺陷检测问题，提出基于机器视觉与深度学习的缺陷检测方法。通过航天太阳电池缺陷检测系统获取图像，并依据企业电池片缺陷的分类标准构建航天太阳电池缺陷数据集。采用切片技术获取包含缺陷目标的子图像数据集，解决卷积和下采样操作信息丢失而导致召回率低的问题。针对不同缺陷采取适当的图像增强方式进行扩充数据集，以避免训练过程中因数据集不足导致的过拟合问题。采用深度可分离卷积、优化损失函数、双线性插值上采样及引入注意力机制等方法对YOLOX-s算法进行改进，以获得综合效果最佳的航天太阳电池缺陷检测模型。通过不同数据集训练及检测精度指标对比，以及消融实验验证改进模型的有效性。通过改进模型与同类主流模型对比实验，验证改进模型在航天太阳电池缺陷检测方面的优越性。Abstract: Aiming at the problems of defect detection for aerospace solar cells, the machine vision and deep learning are combined to detect the surface defect of solar cells. The aerospace solar cells images are obtained through the vision detection system and the aerospace solar cells defect dataset is constructed according to the enterprise’s defect classification standard. So as to solve the problem of low recall rate caused by information loss of convolution and down sampling, the slicing technique is used to obtain the partial defect images of solar cells and the sub-image dataset is constructed. In order to avoid the overfitting problem caused by insufficient dataset in the model training process, the appropriate image enhancement methods are adopted to expand the dataset for different defects. The YOLOX-s algorithm is improved by using depth wise separable convolution, optimizing the loss function, adopting bilinear interpolation up sampling, and introducing convolutional block attention module, and the best comprehensive defect detection model for aerospace solar cells has been obtained. The effectiveness of the improved model has been verified through comparison of multiple detection accuracy indicators between the models trained by different datasets, as well as ablation experiments. The superiority of the improved model for aerospace solar cells defect detection is verified through comparative experiments between similar mainstream models.
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Keywords:
- solar cells /
- machine vision /
- deep learning /
- YOLOX-s /
- defect detection
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