A Deep Learning-Based Surface Defect Inspection System Using Multiscale and Channel-Compressed Features

In machine vision-based surface inspection tasks, defects are typically considered as local anomalies in homogeneous background. However, industrial workpieces commonly contain complex structures, including hallow regions, welding joints, or rivet holes. Such obvious structural interference will inevitably cause a cluttered background and mislead the classification results. Moreover, the sizes of various surface defects might change significantly. Last but not least, it is extremely time-consuming and not scalable to capture large-scale defect data sets to train deep CNN models. To address the challenges mentioned earlier, we first proposed to incorporate multiple convolutional layers with different kernel sizes to increase the receptive field and to generate multiscale features. As a result, the proposed model can better handle the cluttered background and defects of various sizes. Also, we purposely compress the size of parameters in the newly added convolutional layers for better learning of defect-related features using a limited number of training samples. Evaluated in a newly constructed surface defect data set (images contain complex structures and defects of various sizes), our proposed model achieves more accurate recognition results compared with the state-of-the-art surface defect classifiers. Moreover, it is a lightweight model and can deliver real-time processing speed (>100 frames/s) on a computer equipped with a single NVIDIA TITAN X Graphics Processing Unit (12-GB memory).