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Defect detection method using deep convolutional neural network, support vector machine and template matching techniques

机译：使用深度卷积神经网络的缺陷检测方法，支持向量机和模板匹配技术

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摘要

In this paper, a defect detection method using deep convolutional neural network (DCNN), support vector machine (SVM) and template matching techniques is introduced. First, a DCNN for visual inspection is designed and trained using a large number of images to inspect undesirable defects such as crack, burr, protrusion, chipping, spot and fracture phenomena which appear in the manufacturing process of resin molded articles. Then the trained DCNN named sssNet and well-known AlexNet are, respectively, incorporated with two SVMs to classify sample images with high recognition rate into accept as OK category or reject as NG one, in which compressed feature vectors obtained from the DCNNs are used as inputs for the SVMs. The performances of the two types of SVMs with the DCNNs are compared and evaluated through training and classification experiments. Finally, a template matching technique is further proposed to efficiently extract important target areas from original training and test images. This will be able to enhance the reliability and accuracy for defect detection.
机译：介绍了一种基于深度卷积神经网络（DCNN），支持向量机（SVM）和模板匹配技术的缺陷检测方法。首先，使用大量图像设计和训练用于视觉检查的DCNN，以检查在树脂模制品的制造过程中出现的不良缺陷，例如裂纹，毛刺，突起，碎裂，斑点和断裂现象。然后分别将训练有素的名为sssNet和著名的AlexNet的DCNN与两个SVM结合，以将具有较高识别率的样本图像分类为OK类或NO类，其中从DCNN获得的压缩特征向量用作SVM的输入。通过训练和分类实验比较和评估了两种具有DCNN的SVM的性能。最后，进一步提出了一种模板匹配技术，以从原始训练和测试图像中有效提取重要目标区域。这将能够提高缺陷检测的可靠性和准确性。

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