Defect-aware transformer network for intelligent visual surface defect detection. (January 2023) - Shang, Hongbing; Sun, Chuang; Liu, Jinxin; Chen, Xuefeng; Yan, Ruqiang
Abstract: Surface defect detection plays an increasing role in intelligent manufacturing and product life-cycle management, such as quality inspection, process monitoring, and preventive maintenance. The existing intelligent methods almost adopt convolution architecture, and the limited receptive field hinders performance improvement of defect detection. In general, a larger receptive field can bring richer contextual information, resulting in better performance. Although operations such as dilated convolution can expand the receptive field, this improvement is still limited. Recently, benefitting from the ability to model long-range dependencies, Transformer-based models achieve great success in computer vision and image processing. However, applying Transformer-based models without modification is not desirable because there is no awareness and pertinence to defects. In this paper, an intelligent method is proposed by using defect-aware Transformer network (DAT-Net). In DAT-Net, Transformer replaces convolution in encoder to overcome the difficulty of modeling long-range dependencies. Defect-aware module assembled by basic weight matrixes is incorporated into Transformer to perceive and capture geometry and characteristic of defect. Graph position encoding by constructing a dynamic graph on tokens is designed to provide auxiliary positional information, which brings desired improved performance and fine adaptability. Specially, we carry out field experiments and painstakingly construct blade defect and tool wear datasets to compare DAT-Net with other methods. The comprehensive experiments demonstrate that DAT-Net has superior performance with 90.19 mIoU on blade defect dataset and 87.24 mIoU on tool wear dataset.