Semantic Segmentation of Vehicle Steering Column Pattern Image Based on K-Net Network

As an important assembly part of the automobile, the steering column module is mainly used to control the turn signal and the wiper switch. Therefore, it is necessary to check the integrity of its printed beacon pattern. However, the image quality of the traditional image processing vision system is easily affected by the field environment, and due to the existence of a large number of reflection areas in the image, the image segmentation is low precision, low adaptability and high error rate. Therefore, this paper uses K-Net deep learning network semantic segmentation model to automatically remove image reflection. By using Swin-T backbone network and introducing self-attention mechanism, the segmentation performance of beacon is further improved, and the anti-reflective binary segmentation of beacon is realized. Finally, the superiority of the above model is verified in the production line data set of enterprise steering column products. The experimental results show that: (1) K-Net network has a good segmentation effect, and its mIoU reaches 95.77%; (2) After using the Swin-T backbone network, the mIoU of K-Net model increased by 1.1% compared with the traditional CNN backbone network.