Akademik Veri Yönetim Sistemi
2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, İstanbul, Türkiye, 12 - 13 Haziran 2020
Visual inspection systems (VISs) are one of the key technologies needed for mass production in the manufacturing industry. Fast and accurate algorithms are required for quality control of large quantities of products. If the manufactured products change over time inspection of the products becomes a challenging task. In this paper, Split-Brain Convolutional Autoencoder approach to detect and localize defects without defective samples is proposed. Two disjointed Convolutional Autoencoder networks are employed to predict the subchannel of the image from another subchannel. The reconstruction residual maps generated from each subchannel are combined and then thresholded to produce the segmented out image patches as the ultimate result. This approach greatly alters the reconstruction abilities of Convolutional Autoencoders. Experimental results achieved using a challenging dataset have shown that this approach can achieve good defect detection and defective region segmentation results.