Detection and Avoidance of Barriers in Driverless Cars using Deep Learning Techniques

Abstract

In the advance of the technology and implantation of Internet of Things (IoT), the realization of smart city seems to be very needed. One of the key parts of a cyber-physical system of urban life is transportation. Such mission-critical application has led to inquisitiveness in researchers to develop autonomous robots from academicians and industry. In the domain of autonomous robot, intelligent video analytics is very crucial. By the advent of deep learning many neural ¬¬¬networks-based learning approaches are considered. One of the advanced Single Shot Multibox Detector (SSD) method is exploited for real-time video/image analysis using an IOT device and vehicles/any barrier avoidance on road is done using image processing. The proposed work makes use of SSD algorithm which is responsible for object detection and image processing to control the car, based on its current input. Thus, this work aims to develop real-time barrier detection and barrier avoidance for autonomous robots using a camera and barrier avoidance sensor in an unstructured environment.