基于改进YOLOv8的指针式仪表读数识别算法OA

To address the issues of existing deep learning-based meter reading algorithms on edge devices,such as high resource consumption,the lack of robustness,error accumulation,and difficulty in end-to-end pointer extrac-tion in traditional image processing methods in complex scenarios,a lightweight improved YOLOv8 model was pro-posed for meter detection.Meanwhile,the YOLOv8-pose keypoint model was employed to extract keypoints from the meter dial,and the reading was calculated using an angle-based method by fitting the pointer and scale lines.Firstly,a lightweight RGELAN module was designed to replace the C2f module,reducing the complexity of the backbone and neck networks.Then,the CASC-Head detection head,which considered multi-scale feature contri-butions,replaced the decoupled head,reducing detection parameters.Finally,the Shape-IoU optimized regression loss was introduced to improve detection accuracy.Experimental results showed that the improved YOLOv8-RSS model achieved 98.5%precision and 90.6%mAP@50:95,with only 0.3%and 0.4%losses compared with the o-riginal YOLOv8,while reducing parameters,computation,and model size by 48.3%,44.4%,and 46%,respec-tively.In complex scenarios,it achieved an average relative error of 1.425%,average absolute error of 0.557%,3.08 MB parameters,and 78 frame per second.Compared with existing methods,the proposed algorithm reduced space consumption and reading errors,and improved detection speed.