Yazar "Mercimek, Muharrem" seçeneğine göre listele

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    A Nested Autoencoder Approach to Automated Defect Inspection on Textured Surfaces
    (Sciendo, 2021) Öz, Muhammed Ali Nur; Kaymakçı, Özgur Turay; Mercimek, Muharrem
    In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.
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    Anomaly localization in regular textures based on deep convolutional generative adversarial networks
    (Springer, 2022) Öz, Muhammed Ali Nur; Mercimek, Muharrem; Kaymakçı, Özgur Turay
    Pixel-level anomaly localization is a challenging problem due to the lack of abnormal training samples. The existing adversarial network methods attempt to segment anomalies by reconstructing the image then comparing the reconstructed image with the original. However, reconstructing an image with adversarial networks involve complex training procedures and result in long run-times. This paper proposes a simpler and intuitive anomaly localization approach based on generative adversarial networks (GAN) for regular textured images. In the proposed method, a discriminator network generates an anomaly map and is trained by a generator network that generates imitations of anomalous samples. To lower computational costs, strided convolutions are used in the discriminator network to produce anomaly map for pixel blocks instead of individual pixels. Discriminator that is trained in the proposed scheme gains ability to segment the anomalies in images. The experimental results show that the performance of the proposed method is almost equivalent to that of the state-of-the-art methods. Besides, with an accompanying low-cost training phase it is faster and simpler to implement.
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    Automated fabric inspection system development aided with convolutional autoencoder-based defect detection
    (2024) Mercimek, Muharrem; Öz, Muhammed Ali Nur; Kaymakçı, Özgür Turay
    Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.