变电站电力仪表智能检测算法研究

Research on Intelligent Detection Algorithms for Substation Power Instruments

移动边缘端设备在变电站电力仪表检测中,难以快速检测复杂环境中高相似度目标。为此,提出了一种基于轻量级YOLOX网络的电力仪表图像检测方法。首先,搭建YOLOX检测网络,并设计了基于深度可分离卷积骨干特征提取结构和参数重组的多尺度特征融合结构,以压缩模型参数量和提升推理速度。其次,在特征融合层中嵌入3维注意力机制SimAM,通过学习特征的能量分布,对目标区域进行加权,提升复杂环境下的仪表检测能力。同时,针对电力仪表检测的特殊需求,设计了基于金字塔池化特征编码的Transformer结构,从局部特征细化和长距离特征捕获2个方面挖掘底层高语义信息特征,提高不同外形电力仪表的检测精度。最后,通过构建破损、模糊及正常3种类型的电力指针型仪表数据集进行验证。实验结果显示,改进的模型相比原始模型,均值精度从75.49%提升至85.93%,检测速度从36帧/s提升至45帧/s。在移动端硬件Jetson NX上,推理速度可达17.6帧/s。与其他轻量化模型相比,该模型在检测精度和速度上具有明显优势,为电力仪表的可视化、信息化和智能化提供了可行的技术方案。

It is difficult for mobile edge devices to quickly detect high-similarity targets in complex environments during substation power meter inspections.To address this,we proposed a lightweight YOLOX-based method for power meter image detection.First,we built the YOLOX detection network and designed a depthwise separable convolutional back-bone feature extraction structure and a multi-scale feature fusion structure based on parameter reorganization to compress model parameters and improve inference speed.Secondly,a three-dimensional attention mechanism,SimAM,was em-bedded in the feature fusion layer.This mechanism learns the energy distribution of features and weights the target areas to enhance meter detection in complex environments.Additionally,to address specific issues in power meter detection,we designed a transformer structure based on pyramid pooling feature encoding,focusing on refining local features and cap-turing long-distance features to mine high-semantic information,thereby improving the detection accuracy of power meters with different shapes.Finally,we validated the algorithm by constructing a dataset of broken,blurred,and normal power pointer meters.The experimental results show that the improved model increases the mean precision from 75.49%to 85.93%and the detection speed from 36 Frame/s to 45 Frame/s compared to the original model.On the mobile hard-ware Jetson NX,the inference speed reaches 17.6 Frame/s.Compared to other lightweight models,this model has significant advantages in detection accuracy and speed,providing a feasible technical solution for the visualization,in-formatization,and intelligence of power meters.