Multimodal Collaborative Water-Level Recognition for Water Gauge Images via Improved YOLO26 and Qwen3

Automatic water-level recognition from water gauge images is fundamentally hindered by limited adaptability to dynamic environments and the susceptibility of E-shaped characters to false detection in complex scenes. To address these challenges, this study proposes a multimodal collaborative framework that integrates VT-BM3D denoising, an improved YOLO26 detector, and Qwen3-VL-driven semantic reasoning for robust water-level recognition. The VT-BM3D algorithm was employed to suppress water-surface reflections and background noise while preserving the edge features of scale markings. The YOLO26 baseline was improved with the CBAM attention mechanism and a P2 small-object detection head. Two recognition modes were then established: the YOLO two-stage mode and the YOLO-Qwen3 joint mode. Experimental results demonstrated that the improved YOLO26 achieved an mAP50 of 86.1% and an mAP50:95 of 67.4% in the second-stage detection of E-characters and the waterline, surpassing the baseline by 8.6 and 8.5 percentage points, respectively. In the evaluated field scenarios, both modes delivered absolute errors below 2 cm, maintaining reading stability and strong anti-interference capability under water-surface reflections, tilted views, and complex backgrounds. These results collectively validate the accuracy, robustness, and practical applicability of the proposed framework for water-level recognition.