Tiny Defect Detection in High-Resolution Aero-Engine Blade Images via a Coarse-to-Fine Framework

This article studies the problem of aero-engine blade surface defect detection in large images. The effective method for aero-engine blade surface inspection for this real application is currently lacking since most defects are relatively small. Therefore, the task of aero-engine blade surface defect detection is mainly implemented by experienced operators, which is subjective and time-consuming. Moreover, it is hard to fit the requirements of higher accuracy and efficiency manually. Therefore, an effective and efficient method for aero-engine blade surface defect detection is demanded. To achieve this, we propose a vision-based framework in this study to detect defects in a coarse-to-fine manner. First, the captured raw images are with a high resolution of $2448\times 2048$ to ensure the accuracy of defect detection. The raw images are then cropped into smaller regions and fed into our deep convolution neural network (DCNN) to learn features with high representation. Next, the coarse classifier module is proposed to filter most background regions out. Finally, the defects are located and classified by a fine detector module in the defective images, which are selected by the coarse classifier module. Instead of directly applying a detector, our coarse-to-fine framework can effectively save computation and improve accuracy. In addition, the coarse-to-fine framework can be trained in an end-to-end manner. Compared with classical methods for object detection, our method also achieves state-of-the-art performance for aero-engine blade surface defect detection in terms of accuracy and efficiency. Furthermore, our framework has been applied for practical application in many aero-engine blade production lines.