Details

Autor(en) / Beteiligte

• Lei, Liting
• Zhang, Haifei
• Liu, Qinqin
• Li, Xiujing

Titel

Research on Reading Recognition Algorithm of Industrial Instruments Based on Faster-RCNN

Ist Teil von

• 2021 International Conference on Networking, Communications and Information Technology (NetCIT), 2021, p.148-153

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Industrial instruments are widely used in military, aerospace, industry and other fields, especially in the harsh environment of high temperature, high voltage and high radiation such as substation. According to the classification of counting mode, industrial instruments can be divided into pointer instruments and digital instruments. This paper proposes a simultaneous reading recognition algorithm of pointer and digital multi and multi type instruments based on Faster-RCNN network model. Aiming at the problems of complex background, insensitive to small targets and low detection accuracy of industrial instrument reading recognition system, a detection method of industrial instrument based on Feature Pyramid Network (FPN) and Faster-RCNN network is proposed in this paper. In addition, in order to improve the recognition accuracy, an adaptive training data sampling algorithm is introduced to improve the effectiveness of positive and negative training sample extraction. Finally, the experimental results of industrial instrument reading recognition algorithm are systematically analyzed. The results show that the algorithm can be well applied to multi type industrial instrument reading recognition.