A general-purpose pointer meter reading detection method based on adaptive feature fusion and data augmentation

IF 5.6 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Zheng Wang , Ryojun Ikeura , Zhiliang Zhang , Qiaoyue Li
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引用次数: 0

Abstract

Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for “large background and small target” is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts.

基于自适应特征融合和数据增强的通用指针式抄表检测方法
指针式仪表仍然是变电站内的主要监测仪器。为了解决读取多种类型指针式仪表时模型精度差、读取精度低的问题,本文提出了一种通用级联卷积神经网络,用于精确读取各种类型的指针式仪表。所提出的检测方法包括两个阶段:仪表数字定位和仪表指针拟合。在数字(数字)定位阶段,提出了 "大背景、小目标 "的仪器数据增强方法,以提高模型对数字位置的灵敏度,减少冗余背景信息对模型的干扰。此外,还提出了一种自适应特征融合金字塔结构,以实现更有效的特征融合,增强模糊小目标识别能力。与基线模型相比,本研究提出的方法提高了 0.47% 的目标检测准确率。在我们提出的数据集中,数字位置定位阶段的平均识别准确率为 98.65 %,方形仪器、圆形仪器和刻度不均匀仪器的平均读取误差分别为 0.76 %、0.73 % 和 0.67 %。本文提出的通用检测方法实现了超高精度,可用于读取不同类型和刻度的指针式仪表。与现有的指针式读表方法相比,本文提出的检测方法更加简便有效。此外,它还降低了测量误差,从而增强了其在工业领域的实用性。
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来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
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