Lightweight CNN-Based Framework for Industrial Surface Defect Classification

Industrial surface defect classification is an important part of automated quality inspection systems. For these systems to work, they need to be able to detect surface defects accurately and efficiently to improve product reliability and reduce manufacturing costs. Traditional manual inspection methods are often time-consuming, subjective, and not suitable for fast-paced industrial environments. This study proposes a lightweight convolutional neural network (CNN)-based system for classifying industrial surface defects. The model was made to work well for classification and still be fast enough for real-world use. Keras Tuner was used in the proposed method to find the best hyperparameters. The proposed model was evaluated on the Northeastern University (NEU) Surface Defect Database using 5-fold stratified cross-validation. The results obtained from the experiments are promising since the system yields stable performance on all folds with accuracy scores of 99.72%, 99.17%, 100.00%, 99.17%, and 98.61% respectively. The mean accuracy score is calculated as 99.33%. Also, Grad-CAM visualization revealed that the network focuses on defective regions when processing an input image which supports the reliability and interpretability of the classification process.