Study and Verification on Few-shot Evaluation Methods for AI-based Quality Inspection in Production Lines
With the advent of industry 4.0,the deep integration of manufacturing industry with artificial intelligence(AI)has be-come an important development trend.Industrial quality inspection has emerged as a significant breakthrough point.However,there is currently a lack of standardized methods for evaluating industrial quality inspection products in the industry.The per-formance of various quality inspection products is often opaque,making it difficult to optimize and scale up.In response to this situation,this paper proposes an AI-based industrial quality inspection algorithm evaluation method,which is suitable for the ap-plication needs of production lines in the industrial field.This method can evaluate AI-based industrial quality inspection products and their competitors in situations where the sample size is small and imbalanced.The evaluation method constructs a data set through cross-validation to avoid the problem of large evaluation result fluctuations caused by small and imbalanced data sets.It also uses gray box testing to avoid the subjectivity in evaluation results caused by a single source of data.Furthermore,it formu-lates relevant evaluation indicators based on the actual production needs of the production line,which can truly reflect the detec-tion performance of quality inspection products in the production line application scenario.The proposed method is validated through benchmark evaluation of EL testing products for photovoltaic cells,demonstrating its feasibility and its ability to objec-tively reflect the true performance of various products.Finally,based on the analysis and comparison of the evaluation results,some suggestions are provided for the optimization of AI-based industrial quality inspection products.
Industrial quality inspection by AIDeep learningObject detectionDefect detectionEvaluation methodsPhotovoltaic cell EL inspection
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