基于改进AdvancedEAST的变电站仪表数显区域检测  

Detection of digital display area for substation instrument based on improved AdvancedEAST

作　　者：王嘉璇 王天宁 刘兵 王祝 Wang Jiaxuan;Wang Tianning;Liu Bing;Wang Zhu(Department of Automation,North China Electric Power University,Baoding 071003,China;Tianjin Xinsong Intelligent Technology Co.,Ltd.,Tianjin 301800,China)

机构地区：[1]华北电力大学自动化系,保定071003 [2]天津新松智能科技有限公司,天津301800

出　　处：《电子测量技术》2024年第5期45-53,共9页Electronic Measurement Technology

基　　金：国家自然科学基金(U21A20486)项目资助。

摘　　要：为了提高数字仪表数显区域检测的实时性和鲁棒性,提出一种改进AdvancedEAST算法,以快速、准确地完成变电站数字仪表数显区域检测。首先,在AdvancedEAST模型框架下,构建一种注意力机制增强的轻量级主干网络ES-MobilenetV3,通过考虑不同层提取特征对结果的影响,引入ECA和多维注意力机制(ECA-SE)对MobileNetv3的Bneck块进行改进,在保持性能和复杂性平衡的前提下,突出关键特征。在颈部网络中引入深度可分离卷积,降低网络计算的复杂度,提高检测速度。同时,使用迁移学习策略,提高小样本下模型的泛化能力。最后,在构建的变电站数字仪表数据集上进行试验验证,结果表明,所提算法在保证检测精度的情况下,模型的参数量相比AdvancedEAST算法减少82%,检测速度提升近2倍。To enhance the real-time and robustness of digital instrument digital display area detection,an improved AdvancedEAST algorithm is proposed to complete the detection of substation digital instrument digital display area quickly and accurately.First,under the framework of the AdvancedEAST model,an ES-MobilenetV3 lightweight backbone network enhanced by the attention mechanism is constructed.By considering the influence of different layers of extracted features on the results,the ECA and multi-dimensional attention mechanism(ECA-SE)is introduced to The Bneck block of MobileNetv3 is improved to highlight key features while maintaining a balance between performance and complexity.A depthwise separable convolution is introduced in the neck network to reduce the computational complexity of the network and improve the detection speed.At the same time,the transfer learning strategy is used to improve the generalization ability of the model under small samples.Finally,the experimental verification was carried out on the constructed substation digital instrument dataset.The results showed that the proposed algorithm reduced the number of parameters of the model by 82%compared to the AdvancedEAST algorithm and increased the detection speed by nearly 2 times while ensuring detection accuracy.

关 键 词：数字仪表 AdvancedEAST 数显区域检测 注意力机制增强 深度可分离卷积 迁移学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP216[自动化与计算机技术—计算机科学与技术]