Transfer Learning for Real-Time Surface Defect Detection With Multi-Access Edge-Cloud Computing Networks

The development of deep learning and edge computing provides rapid detection capability for surface defects. However, components produced in actual industrial manufacturing environments often have tiny surface defects and training data for each specific defect type is limited. Meanwhile, network resources at the edge of industrial networks are difficult to guarantee. It is challenging to train a proper surface defect detection model for each specific surface defect type and provide a real-time surface defect detection service. To address the challenge, in this paper, we propose a real-time surface defect detection framework based on transfer learning with multi-access edge-cloud computing (MEC) networks. Furthermore, we improve the original YOLO-v5s framework by introducing the spatial and channel attention mechanism, and adding an additional detection head to enhance the detection ability on tiny surface defects. Evaluation results demonstrate that the proposed framework has superior performance in terms of improving detection accuracy and reducing detection delay in the considered MEC network.