DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models
This addresses the need for robust meter reading in industrial monitoring, but it is incremental as it builds on existing vision-language models with a domain-specific adaptation.
The paper tackles the problem of accurately reading pointer meters in smart power systems by introducing a new large-scale dataset, RPM-10K, with 10730 images, and proposing a vision-language model, MRLM, that encodes geometric and causal relationships to improve recognition, achieving validated effectiveness in experiments.
The precise reading recognition of pointer meters plays a key role in smart power systems, but existing approaches remain fragile due to challenges like reflections, occlusions, dynamic viewing angles, and overly between thin pointers and scale markings. Up to now, this area still lacks large-scale datasets to support the development of robust algorithms. To address these challenges, this paper first presents a new large-scale benchmark dataset for dial reading, termed RPM-10K, which contains 10730 meter images that fully reflect the aforementioned key challenges. Built upon the dataset, we propose a novel vision-language model for pointer meter reading recognition, termed MRLM, based on physical relation injection. Instead of exhaustively learning image-level correlations, MRLM explicitly encodes the geometric and causal relationships between the pointer and the scale, aligning perception with physical reasoning in the spirit of world-model perspectives. Through cross-attentional fusion and adaptive expert selection, the model learns to interpret dial configurations and generate precise numeric readings. Extensive experiments fully validated the effectiveness of our proposed framework on the newly proposed benchmark dataset. Both the dataset and source code will be released on https://github.com/Event-AHU/DialBench