Image Semantic Segmentation based on E-Net with Different Patch-Size Convolution

Abstract

Computer Vision maybe those fields that aides machine over the understanding of features of images and videos. It is making massive advances in the field of self-driving, robotics, and automation. In the self-driving car, a navigation system must be there to make a decision. The navigation system has to scan the view to decide the next move. By segmenting the image using Semantic Segmentation the navigation system can make a clear decision. Semantic segmentation is the primary step in object detection. In this research paper, various networks do the semantic segmentation are explained. While using different patch size objects can be more utilized. Here in this research demonstrated different patch sizes 3x3, 4x4, 5x5, and 6x6 for training ENET. Deep Neural Network gives the best result in semantic segmentation in terms of Intersection over Union parameter. In the results and analysis section, the results are generated by changing the patch size from the CityScape dataset images. From the analysis, it has been said that 5×5 patch size gives the highest Intersection over Union compared to other deep networks.