针对太阳能电池板在生产过程中出现的裂缝问题，在太阳能电池板缺陷数据集有限的条件下，提出应用粒子群算法（Particle Swarm Optimization, PSO）优化支持向量机（Support Vector Machines, SVM）的太阳能电池板裂缝缺陷检测算法。首先，为减少图像采集过程中由电致发光（Electroluminescence, EL）检测产生的光照分布不均影响，对太阳能电池板组件图像进行Retinex增强处理；其次，在频域上利用Gabor变换对图像进行纹理特征提取，以获取裂缝特征；最后，将各个太阳能电池板组件的纹理特征经主成分分析法（principal component analysis, PCA）降维后输入到粒子群-支持向量机（Particle Swarm Optimization_Support Vector Machines, PSO_SVM）系统中进行分类识别。应用该方法对600幅太阳能电池板电致发光（EL）图像进行实验，仅有1幅出现误检，分类识别准确率为99.33%。将该算法与决策树分类、极限学习机、卷积神经网络及SVM算法进行对比实验，PSO_SVM获得最高识别准确率。
Aiming at the problem of cracks in solar cells during the production process and under the condition of limited database of solar cell defects, the particle swarm optimization (PSO) is applied to optimize the support vector machines (SVM) to detect the surface cracks of solar cells. Firstly, in order to reduce the influence of uneven light distribution caused by electroluminescence (EL) detection in the image acquisition process, Retinex enhancement processing is performed on the image of the solar cell assembly. Secondly, in the frequency domain, the Gabor transform is used to extract the texture features of the image to obtain the crack feature. Finally, the texture features of each solar cell component are reduced by principal component analysis (PCA) and then they are input into the PSO_SVM system for classification and recognition. Using this method to experiment with 600 EL images of solar cells, only one image was detected by mistake, and the classification accuracy is 99.33%. Comparing this algorithm with decision tree classification, Extreme learning machine (ELM), Convolutional Neural Network (CNN) and SVM algorithm, PSO_SVM achieves the highest recognition rate.