Instance Segmentation for Autonomous Vehicle

Abstract

To detect the objects around an autonomous vehicle is the most important thing to drive it carefully without danger. This paper delivers a presentation on detecting and classifying the objects for assisting autonomous driving. In autonomous driving systems, the task of detecting the vehicle itself is one of the most important prerequisites to autonomous navigation. Deep learning, one of the computer vision tasks, performs object detection very effectively compared to earlier methods and this project is to segment the objects like vehicles, persons. Instance segmentation an extension of object detection is used to identify the objects in the image by assigning different labels for each instance of the objects belonging to the same class. Instance segmentation method is used for solving both object detection and semantic segmentation in parallel. In the existing system like Deep Neural Network (DNN) is used for segmenting the papers in the medical field and in other places. But when it comes to detect for autonomous vehicle it is not detecting clearly. To get better from this problem in the proposed system Instance segmentation is performed by using the method known as Mask RCNN. Compared to earlier detection approaches Mask RCNN shows improvement in detection accuracy and in time complexity.