In this paper, binary classification methods using support vector machines (SVM) obtained from one-class learning and two-class learning are introduced. Firstly, an application of deep CNN (DCNN) for visual inspection is developed and is trained using a large number of images to inspect undesirable defects seen in resin molded articles. Then, the trained DCNN named sssNet and well-known Alexnet are respectively incorporated with two kinds of one-class learning based SVMs to classify sample images with high recognition rate into accept as OK category or reject as NG category, in which compressed feature vectors obtained from the DCNNs are used as the inputs for the SVMs. The performances of two SVMs obtained from one-class learning are compared and evaluated through training and classification experiments. Then, another SVM obtained from two-class learning is introduced. Finally, a template matching technique is further applied to extract important target areas from original training and test images. This will be able to enhance the reliability and accuracy for binary classification.