Automatic Inspection System for Segregation of Defective Parts of Heavy Vehicles

Abstract

Defect Detection is a crucial process in the manufacturing industry. Most of the manufacturing parts tend to get scratches, dents, etc. on their surface during the manufacturing process. Such parts are defective and are not acceptable for further use. So, it is essential to inspect parts before they can be dispatched further. To detect such defects, a team of skilled persons is deployed; which does manual visual inspection of parts to detect those defects. In manual inspection, chances of missing small defects are pretty high. Not only that but it also takes a considerable amount of time. Moreover, the job is tedious and monotonous creating strain on the eyes of members of the inspection team. Some defects are even not visible to the naked eye. To make the process of visual inspection simple and easy, a deep learning Convolutional neural network (CNN) based model is proposed. Reason behind choosing a convolutional neural network is its capability to extract features efficiently as the task accuracy depends upon this capability. A custom dataset has been prepared with utmost care of illumination conditions, resolution of image, etc., so that a clear picture of parts with minute details can be captured. The model has been trained using the dataset thus prepared and containing 960 images and it is observed that it provides a good accuracy of 95%. The same model is deployed using an embedded board with a Jetson Nano processor to prepare a computer vision-based inspection system that can be used to differentiate between perfect and imperfect parts and thus help skilled personnel in the inspection process.