Name
Title | ||
|---|---|---|
Pgc-Net: A Light Weight Convolutional Sequence Network For Digital Pressure Gauge Calibration |
Abstract | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2938106 | IEEE ACCESS |
Keywords | DocType | Volume |
Digital pressure gauge calibration, automatic meter reading, sequence text recognition, light weight CNN, digital gauge dataset | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Li | 1 | 1 | 1.04 |
| Yong Li | 2 | 47 | 2.98 |
| Kechao Lian | 3 | 0 | 0.34 |
| Xiaoyu Bian | 4 | 1 | 1.04 |
| Kuan Yang | 5 | 1 | 1.04 |
| Yongzhi Tian | 6 | 0 | 0.34 |