PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

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burl audio b2 bomber Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions.Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computation-efficient and accurate.In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net).PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework.

Experimental results show that PGC-Net runs 28 fps on CPU sauceland with 97.41% accuracy.Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time.Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net.

We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration.The dataset has been public for future research.