An Online Deep Learning Based System for Defects Detection in Glass Panels

摘要

Automated surface anomaly inspection for industrial application is assuming every year an increasing importance, in particular, deep learning methods are remarkably suitable for detection and segmentation of surface defects. The identification of flaws and structural weaknesses of glass surfaces is crucial to ensure the quality, and more importantly, guarantee the integrity of the panel itself. Glass inspection, in particular, has to overcome many challenges, given the nature of the material itself and the presence of defects that may occur with arbitrary size, shape, and orientation. Traditionally, glass manufacturers automated inspection systems are based on more conventional machine learning algorithms with handcrafted features. However, considering the unpredictable nature of the defects, manually engineered features may easily fail even in the presence of small changes in the environment conditions. To overcome these problems, we propose an inductive transfer learning application for the detection and classification of glass defects. The experimental results show a comparison among different deep learning single-stage and two-stage detectors. Results are computed on a brand new dataset prepared in collaboration with Deltamax Automazione Srl.