An Attention based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Alkhaled, Lama; Roy, Ayush; Palaiahnakote, Shivakumara

Abstract

Digital water meter digit recognition from images of water meter readings is a challenging research problem. One key reason is that this might be a lack of publicly available datasets to develop such methods. Another reason is the digits suffer from poor quality. In this work, we develop a dataset, called MR-AMR-v1, which comprises 10 different digits (0 to 9) that are commonly found in electrical and electronic water meter readings. Additionally, we generate a synthetic benchmarking dataset to make the proposed model robust. We propose a weighted probability averaging ensemble-based water meter digit recognition method applied to snapshots of the Fourier transformed convolution block attention module (FCBAM) aided combined ResNet50-InceptionV3 architecture. This benchmarking method achieves an accuracy of 88% on test set images (benchmarking data). Our model also achieves a high accuracy of 97.73% on the MNIST dataset. We benchmark the result on this dataset using the proposed method after performing an exhaustive set of experiments.

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2023
• Publication Date: Oct 24, 2023
• Deposit Date: Nov 15, 2024
• Publicly Available Date: Nov 18, 2024
• Journal: Artificial Intelligence and Applications
• Print ISSN: 2811-0854
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.47852/bonviewAIA32021197