Electric Energy Meter Reading Detection Algorithm Based on Improved YOLOv8n Model

In smart grid systems, automated electric meter reading technology is crucial for optimizing the management and distribution of electricity resources. In this study, an optimized algorithm based on the YOLOv8n model is introduced for electric meter reading recognition, with the goal of improving both the precision and performance of automated meter reading systems. This algorithm incorporates the SPDConv as a substitute for the conventional convolution layers in the original model, which significantly minimizes information loss while boosting the model's feature extraction efficiency. Moreover, the backbone network integrates a lightweight channel attention mechanism known as ECA, which enhances attention to critical local features and improves the detection performance for small objects. The experimental evaluation on a tailored electric meter dataset reveals that the enhanced algorithm surpasses the YOLOv8n model, achieving a 4.6% improvement in mAP, along with reductions of 10.6% in parameter count, 10% in model size, and 7.4% in computational cost.