Deep Learning Neural Networks-Based Edge Detection in Mechanical Components

For the past few decades, the traditional deep neural network was one of the vital parts of machine learning methods, but in recent years the trend has been changed with the development of machine vision technology.Nowadays convolutional neural network is used in most image detection-related sectors.Detecting the special features from the images such as hole detection, edge detection, or detection of any corners can be easily and effectively done using the convolutional neural network.The system can estimate and identify the location of possible objects for object detection.This research aims to detect the special features from the images and determine the coordinates of the edges then convey those data to the industrial computer, which is used for the accurate assembling of the mechanical parts in the industry.The research is started with capturing the images of the mechanical disc.Next, the targeted parts of the images subjected to be detected are labeled using image labeling tools.In the next step, all the images are used in training.After the training, the weight value obtained from the training is used in the python code for detecting the features.A graphical user interface is made for operating with the data and result demonstration.In the result section, all the required edges are identified correctly in the first step.In the second step, the mean distances between the two edges are determined using the Euclidean method.Lastly, the holes are detected and the center and the coordinates of the center of the holes are also identified.