Hybrid Deep Learning Framework for Accurate Surface Defect Detection Using Autoencoder and CNNs

Abstract

Surface defect detection is paramount in industrial quality control. Conventional methods, which often rely on human inspection or manually engineered statistical models, frequently fail to accurately detect and classify defects, particularly on complex surfaces or those with intricate features. Human inspection is inherently inconsistent and prone to errors due to fatigue, while traditional machine vision systems often lack the sensitivity to clearly identify small or low-contrast defects. This paper proposes a hybrid deep learning framework, termed Autoencoders-Convolutional Neural Networks (AE-CNNs), DefectNet, for surface defect classification, AE, and CNNs to enhance both accuracy and efficiency. The AE is employed to extract and compress reliable features from surface images into latent representations, which are subsequently classified by a CNN enhanced through transfer learning using InceptionV3. The CNN is fine-tuned from a pretrained model with customized fully connected layers to adapt to specific defect characteristics, while the AE is trained exclusively on non-defective images. The encoded features produced by the AE serve as the input to the CNN. The proposed model is evaluated on standard benchmark datasets comprising diverse surface defect types and compared against Anomaly Detection with Autoencoder (ADA), Visual Geometry Group (VGG16), Inception-based Convolutional Neural Network Long Short-Term Memory (In-CNNLSTM), and DTL_Inception_v3. Experimental results demonstrate the superior performance of the proposed method, achieving classification accuracies ranging from 85.60% to 100% across five datasets, including a perfect 100% accuracy on the glass bottle neck dataset.