Deep Learning for Automatic Vision Based Recognition of Industrial Surface Defects

Abstract

In today’s industrial landscape, traditional defect detection methods rely heavily on manual inspection or machine learning models with handcrafted features, which are often limitedby subjectivity, and low accuracy, and poor adaptability to complex surface These conventional approaches struggle with subtle or small defects, require extensive human effort, and are not scalable for real time production environments. This project overcomes the drawbacks of conventional inspection methods by introducing a fully automated, vision-based defect detection system powered by deep learning. By utilizing convolutional neural networks (CNNs), the system is capable of learning relevant features directly from raw images, enabling more accurate, consistent, and faster defect identification. Transfer learning is employed to address limited training data, while explainable learning techniques are integrated to enhance transparency and build confidence in the system’s predictions. Overall, the proposed approach offers a reliable, scalable, and practical solution for industrial quality inspection applications.