Machine Vision-Driven Semantic Segmentation for Autonomous Navigation

One of the main roles played by real time image segmentation is to enhance and catalyse self driving cars that can accurately sense their surroundings due to in terms of proper functioning. The new model was proposed with a completely new image segmentation paradigm for enabling self-driving in real time. The deployment of deep learning techniques, accurate real-time object segmentation through a fusion of fully convolutional network (FCN) with multilevel pyramid analysis. FCN uses the input image for segmentation, and in return creates a map-based on pixel-wise classification from which we can get more detailed information about different objects present in the scene. This approach creates a very large FCN network that is combined with a multiscale pyramid architecture that process an image in multiple stages effectively collecting complex surroundings information to improve performance. The proposed method is then experimented on different data with a particular focus on challenging to adopt such infrastructure in order to achieve modern day results in terms of accuracy and real time performance.