A Pointer Meter Bilateral Image Segmentation Network Integrating Spatial Details and Semantic Features

Aiming at the characteristics of small target image segmentation of pointer meter and the limitations of existing methods, a bilateral deep learning backbone network called BiUnet is proposed for pointer meter image segmentation, which combines spatial details and semantic features. Starting from BiSeNet V2 algorithm, the semantic branch, detail branch and bilateral fusion layer are redesigned in this network. First, the ConvNeXt convolution block is used to adjust and optimize the detail branch to improve the feature extraction ability of the algorithm for pointer and scale line boundary details. Second, the semantic branch is redesigned based on the advantages of the U-shape structure of encoder and decoder to integrate different scales of semantic information, which improves the special segmentation ability of the semantic branch for small objects such as pointer and scale. Finally, a bilateral- guide splicing aggregation layer is proposed to fuse the detail branch and the semantic branch features. The ablation experiments on the self- made instrument image segmentation dataset confirm the validity and feasibility of the proposed network design scheme. Comparative experiments with different backbone networks are carried out on the instrument dataset, the experimental results show that the mIoU (mean intersection of union) of BiUnet's instrument segmentation accuracy reaches 88. 66%, which is 8. 64 percentage points higher than the BiSeNet V2 network (80. 02%). Both of them have better segmentation accuracy than common backbone networks based on Transformer and pure convolution.