Research on the application of deep learning in automatic reading recognition of pointer meters
深度学习在指针式仪表自动读数识别中的应用研究

摘要

指针式仪表广泛应用于工业、电力、交通等关键领域，其读数的准确识别对于系统监控和故障预警具有重要意义。传统人工读数方式效率低、易出错，难以适应智能化发展需求。本文围绕“深度学习在指针式仪表自动读数识别中的应用”展开研究，提出基于深度学习模型的图像处理方法，旨在实现对复杂环境下指针式仪表的高精度、自动化识别。通过引入卷积神经网络与目标检测技术，提升指针对比度低、角度变化大等问题下的识别能力，验证了深度学习在该领域的可行性与优越性。

Abstract

Pointer meters are widely used in key fields such as industry, electricity, and transportation. The accurate recognition of their readings is of great significance for system monitoring and fault early warning. Traditional manual reading methods are inefficient and error-prone, making it difficult to meet the needs of intelligent development. This paper focuses on the research of "application of deep learning in automatic reading recognition of pointer meters" and proposes an image processing method based on deep learning models, aiming to achieve high-precision and automated recognition of pointer meters in complex environments. By introducing convolutional neural networks and target detection technologies, the recognition capability under issues such as low pointer contrast and large angle changes is improved, verifying the feasibility and superiority of deep learning in this field.