Hybridformer: an efficient and robust new hybrid network for chip image segmentation

Intelligent monitoring of machine chips can help the machine tool to remove chip accumulation in time. Based on chip shape information, real-time monitoring of the machine tool is also possible. Although image segmentation algorithms have been widely used in industry, they still need to improve. These methods still have some limitations in effectively combining global and local information of industrial images, and there needs to be an image segmentation algorithm specifically for machine tool chips. In this paper, we propose a deep learning-based algorithm for lightweight chip image segmentation named Hybridformer. First, the algorithm generates scale-aware semantic features using feature maps of different scales as the input part. Then, we use the cross specification and SelfNorm to improve the generalization robustness of the model in terms of distribution bias. Finally, the network extracts global contextual information fusing EfficientNetv2 features to achieve accurate localization. Widespread experimental results show that Hybridformer outperforms both CNN and Transformer algorithms in terms of comprehensive performance on multiple chip image datasets, and it can achieve a good balance between accuracy and size of parameters.