基于改进YOLOv8n的字轮式水表数字识别算法OA

In order to improve the efficiency and accuracy of meter readings for word-wheel water meters,a digital recognition algorithm for word-wheel water meters based on improved YOLOv8n is proposed to address the problems of low reading accuracy and excessive parameters.Firstly,a novel GDC2f module is designed by introducing GhostConv and the attention mechanism HDCA(High-Resolution Dual-Channel Attention).This design simplifies the feature extraction network while enhancing the model's ability to extract water meter characters.Secondly,GSConv is employed in place of the original Conv,and a Slim-Neck feature fusion network is introduced,which enhance the feature expression ability for small targets and reduce the number of parameters.Finally,the MPDIoU is adopted to optimize the model,improving the ability of the bounding box localization and the convergence speed of the model.Experimental results show that the improved model increases the precision,recall and average accuracy by 1.3%,2.4%and 3.3%,respectively.It also decreases the computation,parameters and model size by 2.9 GB,0.79×106 and 0.5 MB,respectively.