Defect Detection on Metal Surface Using Deep Learning Algorithms Based on Convolutional Neural Network

Abstract

This project aims to develop a deep learning model utilizing Convolutional Neural Networks (CNNs) to detect and categorize surface defects in metal photos. Traditional manual inspection is typically slow, uneven, and full of mistakes, with a research gap in automated solutions. The objective is to provide an effective and dependable solution for fault identification, crucial in manufacturing and quality control procedures. A proprietary CNN will be trained on a large dataset of labeled pictures, facilitating the extraction of critical characteristics via convolutional, pooling, and fully connected layers. The project will examine the usage of pre-trained models such as VGG16 and ResNet50. These models will be evaluated singly and in combination using an ensemble method to boost classification accuracy and resilience. The models’ performances will be measured based on evolution metrics such as accuracy, loss, and their ability to categorize photos into one of the defect categories. It is predicted that the ensemble strategy would give greater performance compared to individual models, illustrating the usefulness of deep learning-based methods for automated fault identification. The method will give a scalable deep learning framework for defect detection, enabling real time integration in manufacturing industries.