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@article{149774,
        author = {Shreya Pejathaya and Shyamala AA and Jagruth and Neethu S and Dr.B.Muthukumaraswamy and Dr.Prakash Biswagar},
        title = {Application of Semantic Segmentation using OpenCV and Deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {634-640},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149774},
        abstract = {One area that has attained a great progress in computer vison is Object detection using deep learning. This article proposes an article location framework that depends on a multi-locale completely convolutional neural system (FCN) that likewise encodes semantic division mindful highlights and dependent on U-net model.. Semantic segmentation or image segmentation is the unique way of clustering parts of an image together which belong to the same object class. One of the best examples of semantic segmentation is Self-Driving cars. Self-driving cars are also known as autonomous cars and they combine sensors and software to control and navigate the vehicle. The system is developed on python to detect vehicles. The system is trained and tested against images and videos. The system averaged at 20ms to process the image.},
        keywords = {Deep learning, Fully convolutional neural network (FCN), Python, OpenCV, U-Net},
        month = {},
        }