Quality safety monitoring of LED chips using deep learning-based vision inspection methods

Abstract The surface quality safety inspection of a light-emitting diodes (LED) chip is an indispensable step in the LED production process. The traditional vision inspection algorithm extracts defect features by artificial feature extraction. This method has become increasingly difficult as chips have become more miniaturized, and it is difficult to meet the quality safety requirements. Recently, machine learning-based vision detection methods, especially the models of deep convolution neural network (CNN), have made significant breakthroughs in this field, and their performances greatly exceed the traditional model based on manual features. In this paper, a parallel deep convolution model parallel spatial pyramid pooling network (PSPP-net) for LED chip surface quality detection is proposed. Specifically, this model aims at utilizing the advantages of the spatial pyramid pooling (SPP-net) model to online and offline extract two groups of depth-based CNN features through offline training and online recognition of two depth-based CNN data conversion streams. Then, the features are mixed and intersected in the GPU to form 1024-dimensional image feature vectors. Finally, softmax regression is adopted for defect classification and recognition. The problem of off-line feature training extraction and on-line defect recognition in surface quality safety inspection of LED chips is solved.