GMS-YOLO: an enhanced algorithm for water meter reading recognition in complex environments

The disordered arrangement of water-meter pipes and the random rotation angles of their mechanical character wheels frequently result in captured water-meter images exhibiting tilt, blur, and incomplete characters. These issues complicate the detection of water-meter images, rendering traditional OCR (optical character recognition) methods inadequate for current detection requirements. Furthermore, the two-stage detection method, which involves first locating and then recognizing, proves overly cumbersome. In this paper, water-meter reading recognition is approached as an object-detection task, extracting readings using the algorithm’s Predicted Box information, establishing a water-meter dataset, and refining the algorithmic framework to improve the accuracy of recognizing incomplete characters. Utilizing YOLOv8n as the baseline, we propose GMS-YOLO, a novel object-detection algorithm that employs Grouped Multi-Scale Convolution for enhanced performance. First, by substituting the Bottleneck module’s convolution with GMSC (Grouped Multi-Scale Convolution), the model can access various scale receptive fields, thus boosting its feature-extraction prowess. Second, incorporating LSKA (Large Kernel Separable Attention) into the SPPF (Spatial Pyramid Pooling Fast) module improves the perception of fine-grained features. Finally, replacing CIoU (Generalized Intersection over Union) with the ShapeIoU bounding box loss function enhances the model’s ability to localize objects and speeds up its convergence. Evaluating a self-compiled water-meter image dataset, GMS-YOLO attained a mAP@0.5 of 92.4% and a precision of 93.2%, marking a 2.0% and 2.1% enhancement over YOLOv8n, respectively. Despite the increased computational burden, GMS-YOLO maintains an average detection time of 10 ms per image, meeting practical detection needs.