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Publication Index: ScopusSubject: Image processing

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Now showing 1 - 3 of 3
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    Conference Object
    Citation - Scopus: 2
    Detecting Errors in Automatic Image Captioning by Deep Learning;
    (Institute of Electrical and Electronics Engineers Inc., 2021) Karakaya,M.
    Automatic tagging of images is an important researcli topic in tlie field of image processing. Anotlier area similar to this is the automatic generation of picture captions. In this study, a deep learning model that automatically tags the pictures is used to detect errors in image captions. As a result of the initial experiments, it is observed that the proposed system can find up to 80% of the errors in the image captions. © 2021 IEEE
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    Review
    Citation - WoS: 5
    Citation - Scopus: 8
    Research on Pcb Defect Detection Using Artificial Intelligence: a Systematic Mapping Study
    (Springer Heidelberg, 2024) Ural, Dogan Irmak; Sezen, Arda
    SMT (Surface Mount Technology) has been the backbone of PCB (Printed Circuit Board) production for the last couple of decades. Even though the speed and accuracy of SMT have been drastically improved in the last decade, errors during production are still a very valid problem for the PCB industry. With the exponential rise of Artificial Intelligence in the last decade, the SMT industry was one of the most eager industries to use this new technology to detect possible defects during production. Lately, traditional image processing techniques started to lag behind methods such as machine learning and deep learning when the discussion came to the need of high accuracy. In this paper, we screen academic libraries to understand which of the latest methods and techniques are used in the domain and to deduce a general process for detecting defects in PCBs. During the research we have investigated research questions related to state-of-the-art methods, highly mentioned datasets, and sought after PCB defects. All findings and answers are mapped to be able to understand where this pursuit might point towards. From a total of 270 papers, 90 of them were addressed in detail and 78 papers were chosen for this systematic mapping.
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    Article
    Citation - WoS: 85
    Citation - Scopus: 133
    Detecting Cassava Mosaic Disease Using a Deep Residual Convolutional Neural Network With Distinct Block Processing
    (Peerj inc, 2021) Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga; Misra, Sanjay; Damasevicius, Robertas
    For people in developing countries, cassava is a major source of calories and carbohydrates. However, Cassava Mosaic Disease (CMD) has become a major cause of concern among farmers in sub-Saharan Africa countries, which rely on cassava for both business and local consumption. The article proposes a novel deep residual convolution neural network (DRNN) for CMD detection in cassava leaf images. With the aid of distinct block processing, we can counterbalance the imbalanced image dataset of the cassava diseases and increase the number of images available for training and testing. Moreover, we adjust low contrast using Gamma correction and decorrelation stretching to enhance the color separation of an image with significant band-to-band correlation. Experimental results demonstrate that using a balanced dataset of images increases the accuracy of classification. The proposed DRNN model outperforms the plain convolutional neural network (PCNN) by a significant margin of 9.25% on the Cassava Disease Dataset from Kaggle.