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.

发光二极管（LED）芯片的表面质量安全检查是LED生产过程中必不可少的步骤。传统的视觉检查算法通过人工特征提取来提取缺陷特征。随着芯片变得更加小型化，该方法变得越来越困难，并且难以满足质量安全要求。近年来，基于机器学习的视觉检测方法，特别是深度卷积神经网络（CNN）模型在该领域取得了重大突破，其性能大大超过了基于手动特征的传统模型。本文提出了一种用于LED芯片表面质量检测的并行深度卷积模型并行空间金字塔池网络（PSPP-net）。特别，该模型旨在利用空间金字塔池（SPP-net）模型的优势，通过离线训练和两个基于深度的CNN数据转换流的在线识别，在线和离线提取两组基于深度的CNN特征。然后，将特征混合并在GPU中相交以形成1024维图像特征向量。最后，采用softmax回归进行缺陷分类和识别。解决了LED芯片表面质量安全检查中离线特征训练提取和在线缺陷识别的问题。这些特征在GPU中混合并相交以形成1024维图像特征向量。最后，采用softmax回归进行缺陷分类和识别。解决了LED芯片表面质量安全检查中离线特征训练提取和在线缺陷识别的问题。这些特征在GPU中混合并相交以形成1024维图像特征向量。最后，采用softmax回归进行缺陷分类和识别。解决了LED芯片表面质量安全检查中离线特征训练提取和在线缺陷识别的问题。