Sound Level Meter Reading Recognition Method Based on Improved DBNet-CRNN
WANG Jia1,ZHU Haijiang1,WANG Yinchu2,HE Longbiao2,YANG Ping2,NIU Feng2
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2. National Institute of Metrology, Beijing 100029, China
Abstract：In order to improve the work efficiency of the sound level meter calibration work, a reading detection and recognition method based on deep learning neural network for the image of the sound level meter is proposed. The reading detection model is based on DBNet and uses ShuffleNetV2 as the backbone network, significantly reducing the number of model parameters. To improve the accuracy of reading area detection, an efficient channel attention ECA module is introduced to enhance the network's ability to extract channel features. The optimized model reduces the number of parameters to 15.4% and the calculation amount to 67.4% while maintaining accuracy. The reading recognition model is based on the CRNN model, which first adds a batch normalization layer to improve the stability of network training. Then, residual blocks are introduced to replace the original convolutional blocks, improving the network's ability to extract complex features. Applying Dropout to the network to improve its generalization ability. In addition, pre-training the reading recognition model on the synthesized reading dataset effectively increases the accuracy of the model. The improved method achieves an accuracy of 99.7%, which is 2.4% higher than the original method. The experimental results show that this method has strong robustness against factors such as diverse fonts, uneven lighting, and blurring in sound level meter images, and has high recognition accuracy for readings in sound level meter images.
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2024, Vol. 45 
