Application of Semantic Segmentation using OpenCV and Deep learning

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Application of Semantic Segmentation using OpenCV and Deep learning
Author(s):
Shreya Pejathaya, Shyamala AA, Jagruth, Neethu S, Dr.B.Muthukumaraswamy, Dr.Prakash Biswagar
Keywords:
Deep learning, Fully convolutional neural network (FCN), Python, OpenCV, U-Net
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.
Article Details
Unique Paper ID: 149774 Publication Volume & Issue: Volume 7, Issue 1 Page(s): 634 - 640
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