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  • Open Access


    Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

    Shaheen Khatoon1,*, Md Maruf Hasan1, Amna Asif1, Majed Alshmari1, Yun-Kiam Yap2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580

    Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More >

  • Open Access


    A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

    V. Sudha1,*, T. R. Ganeshbabu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008

    Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More >

  • Open Access


    A Lane Detection Method Based on Semantic Segmentation

    Ling Ding1, 2, Huyin Zhang1, *, Jinsheng Xiao3, *, Cheng Shu3, Shejie Lu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 1039-1053, 2020, DOI:10.32604/cmes.2020.08268

    Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses the index function of the… More >

  • Open Access


    Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection

    Princy Matlani1,*, Manish Shrivastava1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985

    Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced.… More >

  • Open Access


    Improved VGG Model for Road Traffic Sign Recognition

    Shuren Zhou1,2,*, Wenlong Liang1,2, Junguo Li1,2, Jeong-Uk Kim3

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

    Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >

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