光伏板缺陷分类的原型网络改进

Prototypical Network Improvement of Photovoltaic Panel Defect Classification

在光伏板缺陷分类领域中,传统的缺陷分类手段和新兴的机器学习方法都存在局限性,不足以满足光伏板缺陷分类需求,急需更可靠的解决方案.近些年来小样本学习以其能在有限量数据下快速学习并泛化到新任务的特点,逐渐在各领域兴起,给缺陷技术的优化带来新的思路.在这里,以典型的小样本学习方法——原型网络方法为基础,提出了基于改进的原型网络的光伏板缺陷分类方法.该方法调整了训练模式,通过改进模型主干网络和相似性度量标准来有效解决原型网络对复杂样本的特征嵌入能力较差和模型精度一般的问题,方法在经典的光伏板缺陷数据集进行了多次对比实验.结果表明:改进方法的实验耗时大大缩短,模型精度得到提高.

In the field of photovoltaic panel defect classification,since traditional defect classification methods and emerging machine learning methods have limitations,which fail to meet the requirements for such classification,more reliable solutions are urgently needed.In recent years,few-shot learning,which can quickly learn from limited data and be generalized to new tasks,has gradually sprung up in various fields,bringing new ideas to the optimization of defect technology.Based on a typical few-shot learning method,the prototypical network method,this study proposes an improved prototypical network-based defect classification method for photovoltaic panels.By complicating the model backbone network,improving the model training mode and adjusting the similarity measurement standard,this method can effectively solve the problems of the poor feature embedding ability and general classification effect of the prototypical network for complex samples.The method has been verified by several comparative experiments on a classic photovoltaic panel defect data set.The results show that the experimental time of the improved method is greatly shortened and the model accuracy is improved.