Improving IoT-based Smart Retrofit Model for Analog Water Meters using DL based Algorithm

Abstract

This paper proposes a deep learning (DL)-based algorithm which is used for improving the performance of digit detection from internet-of-things (IoT)-based analog water meters. The DL algorithm is trained on a rich dataset of over 160,000 images collected from six water nodes deployed at locations with different environmental conditions. A detailed comparison between the proposed DL and machine learning (ML) algorithm is made based on detection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is more generalized in terms of feature extraction, which makes the algorithm robust.