Hu, M., Wu, Y., Yang, Y., et al. (2023) DAGL-Faster: Domain Adaptive Faster R-CNN for Vehicle Object Detection in Rainy and Foggy Weather Conditions. Displays, 79, Article ID: 102484.
https://doi.org/10.1016/j.displa.2023.102484

has been cited by the following article:

• TITLE: Detection of Water Meter Digits Based on Improved Faster R-CNN

  AUTHORS: Liyun Sun, Yuying Yuan, Shichao Qiao, Ruijie Qi

  KEYWORDS: Faster RCNN, Identification of Water Meter Numbers, ResNet50, FPN, ROI Align

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 7, 2024

  ABSTRACT: In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition; the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.