Spray painting after vehicle body inspection is one of the most important aspects of automobile manufacturing and assembling industries. There are many methods available to identify the defects such as dents, scratches or some deformity containing both on a vehicle body using manual labor to closely look and identify the defects and then transfer to spray painting area which may be controlled either manually or semi-autonomously, but this method is not cost- effective as it require more labors and may result in slowing down the speed of manufacturing in a plant leading it to not achieving the target goals set. In this paper, we proposed a defect detection and inspection system using Faster RCNN approach for image processing on a car body to check type of defects apart from the ones which are planned on it and spray painting using a fully autonomous robotic arm having 6 degree of freedom with a sprayer attached to its end effectors and other robotic arm with a camera for inspection. The decision making is provided by classified neural networks through continuous monitoring the vehicle body by the camera. Here the case study takes up the spraying and defect detection in a car body. A similar setup can be used to detect deformity in any other vehicle. © 2021, Springer Nature Singapore Pte Ltd.