Design and Implementation of a Defect Identification Using Image Processing Technique

Abstract

AbstractIn recent days people uses image processing to solve many problems for effective reasons. This chapter describes the work on defect identification of product which has manufactured in industries using image processing. In the existing methods industries uses manual checking by different gauges to identify the defects in products. In this proposed model, image processing method is employed to identify various defects in products. The original images are captured using image sensor for training the dataset. The captured images are classified with different classes and trained using Recurrent Neural Networks (RNN). While real time capturing, first the picture of the product is captured and compared with the existing trained dataset. For testing, gear is employed as a subject material. Here we have classified different cases into three as tooth missing, scratched gear and perfect gear. Python is the base language for the process which takes place for building the dataset and entire flow of the technique. The image processing algorithm is built on TensorFlow to identify the defect with the support of dataset.KeywordsRecurrent Neural Network (RNN)OpenCV2MetricDatasetNeural networks