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Shuning Zhang

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Proceedings Article | 4 April 2023 Paper

Gas cylinder character recognition based on lightweight ResNet model

Jin He, Zijian Yu, Shuning Zhang, Hui He

Proceedings Volume 12617, 1261754 (2023) https://doi.org/10.1117/12.2666449

KEYWORDS: Optical character recognition, Image segmentation, Image processing, Neural networks, Deep learning, Convolutional neural networks

Read Abstract +

Characters pressed on the surface of the liquefied gas cylinder cap are used to mark each cylinder’s identity and record the information about the gas in the cylinder. An automatic character recognition based on a lightweight ResNet model is proposed for the online quality and safety management of cylinder-contained gas production. Firstly, images of pressed characters on the cylinder’s cap are collected under low angle lighting to improve the image contrast of character regions against the background. The connected component method is used to determine the position of each character and then segment the single characters. The pressed-character data set of gas cylinder is constructed by expanding data through image blurring, lightening, darkening and noise adding. According to the data scenario and scale, ResNet18 is selected as the backbone network. To implement the lightweight network model, by removing the last two residual modules in the backbone network, the number of the network layers is reduced to ten. In addition, the number of feature channels is trimmed to further reduce the redundancy of the convolution kernel and improve the calculation efficiency of the network model. Finally, the Max pooling layer is used for all channels to aggregate information and obtain character features. The experimental results show that the recognition accuracy of the proposed network is 94.7% on the constructed gas-cylinder character dataset. Compared with the comparison network, the proposed network not only has higher accuracy and robustness, but also reduces the capacity and computational complexity.

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