An Effective Semantic Segmentation Network With Multipath Attention for Industrial Meter Pointer Images

Meter pointers exhibit stable and anti-interference capabilities, rendering them extensively utilized in industrial environments. However, automated reading poses a significant challenge due to the fact that current segmentation methods struggle to isolate the fine-grained pointers and scales for accurate reading calculations. This challenge can be alleviated by enhancing the feature extraction capability of the segmentation network. As is well-known that Attention plays an essential role in human vision by selectively focusing on convex parts, and attention-based methods have been applied to various computer vision tasks. Therefore, we propose a new image segmentation network called multipath attention network (MPANet) for pointer meter recognition in the complex industrial environments. The designed network employs an attention gate mechanism to proficiently capture local features stemming from various pathways during skip-connection and upsample processes. In addition, our network incorporates deep supervision by merging the outputs of the final three layers to extract abundant low-dimensional information. To further improve the performance of encoders and decoders, a residual U-block is employed, thereby forming an enhanced U-shaped network structure. In the experiments, we employ HD95, Dice, and Recall as evaluation metrics. MPANet demonstrates superior performance compared to state-of-the-art networks on three our self-collected datasets, showing improvements of over 1% across all metrics. In addition, we validate the efficacy of MPA as a plug-and-play module and the benefits of applying deep supervision to multidecoder network.