The study of instrument recognition based on convolutional neural network

Ze Wang, Dan Wu, Le Hua, Su-li Yan, Zhe Gao, Zhao-xue Wu
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Abstract

At present, the digital meter recognition is widely used in the field of power transmission and transformation, but rarely used in the petrochemical industry. This paper combines traditional image processing techniques with deep learning methods and proposes a meter recognition method based on an improved neural network model. By establishing a neural network model architecture, setting up the three convolutional layers and adding a batch normalization processing operation, the digital meters of the oil extraction platform are recognized, and the recognition rate is about 99.17%, which achieve the expectations. Through comparative research, the model constructed in this paper has a higher recognition accuracy and reflects a better recognition effect.
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基于卷积神经网络的仪器识别研究
目前,数字电表识别广泛应用于输变电领域,但在石油化工行业应用较少。本文将传统图像处理技术与深度学习方法相结合,提出了一种基于改进神经网络模型的仪表识别方法。通过建立神经网络模型架构,设置三个卷积层,并加入批归一化处理操作,对采油平台的数字仪表进行了识别,识别率约为99.17%,达到了预期效果。通过对比研究,本文构建的模型具有较高的识别精度,反映出较好的识别效果。
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