Instrument reading recognition by deep learning of capsules network model for digitalization in Industrial Internet of Things

Abstract

AbstractUsing the intelligent image recognition method to recognize the reading of the unintelligent manual reading meter is an important approach to expand the sensing means and reduce the cost of transformation of the Industrial Internet of Things. Traditional methods based on morphology and back propagation neural networks are difficult to solve the problems of shadow, deflection, and dimness in the industrial environment. In this article, the deep learning‐based capsule networks for pointer instruments and digital display instruments were proposed to implement unintelligent manual reading meter recognition using. Due to the complex structure and a large number of parameters, the basic capsule network is difficult to run on devices with low computing resources. To overcome this issue, the improved capsule network model was designed to reduce the parameters of the model. The experimental results show that the improved capsule network improves the recognition accuracy by 13% and reduces the parameters by 23%.