A deep learning-based surface defect inspection system using multi-scale 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 cluttered background and mislead the classification results. Moreover, the sizes of various surface defects might change significantly. Last but not the least, it is extremely time-consuming and not scalable to capture large-scale defect datasets to train deep CNN models. To address the challenges mentioned above, we firstly proposed to incorporate multiple convolutional layers with different kernel sizes to increase the receptive field and to generate multi-scale features. As a result, the proposed model can better handle 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 dataset (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 light-weight model and can deliver real-time processing speed (>100fps) on a computer equipped with a single NVIDIA TITAN X Graphics Processing Unit (12G memory)