Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection

The control of surface defects is of critical importance in manufacturing quality control systems. Today, automatic defects detection using imaging and deep learning algorithms has produced more successful results than manual inspections. Thanks to these automatic applications, manufacturing systems will increase the production quality, and thus financial losses will be prevented. However, since the appearance and dimensions of the defects on the surface are very variable, automatic surface defect detection is a complex problem. In this study, multi-dimensional feature extraction-based deep encoder–decoder network (MFE-DEDNet) network developed to solve such problems. An effective encoder–decoder model with lower parameters compared to the state-of-the-art methods is developed using the depthwise separable convolutions (DSC) layers in the proposed model. In addition, the 3D spectral and spatial features extract (3DFE) module of the proposed model is developed to extract deep spectral and spatial features, as well as deep semantic features. During the combination of these features, the multi-input attention gate (MIAG) module is used so that important details are not lost. As a result, the proposed MFE-DEDNet model based on these structures enabled the extraction of powerful and effective features for defect detection in surface datasets containing few images. In experimental studies, MVTec and MT datasets were used to evaluate the performance of the MFE-DEDNet. The experimental results achieved 80.01% and 56% mean intersection-over-union (mIoU) scores for the MT and MVTec datasets, respectively. In these results, it was observed that the proposed model produced higher success compared to other state-of-the-art methods.