Water Meter Detection System Using YOLOv11 with Variations in Image Augmentation Techniques and Integrated into Telegram

Rajes Khana; Muhammad Sobirin; Ahmad Rofii; Panji Wijonarko; Bobby Arvian James; Rheza Shangajie

doi:10.21771/jrtppi.2026.v17.no1.p84-93

Authors

Rajes Khana University of 17 August 1945 Jakarta
Muhammad Sobirin University of 17 August 1945 Jakarta
Ahmad Rofii University of 17 August 1945 Jakarta
Panji Wijonarko University of 17 August 1945 Jakarta
Bobby Arvian James University of 17 August 1945 Jakarta
Rheza Shangajie University of 17 August 1945 Jakarta

DOI:

https://doi.org/10.21771/jrtppi.2026.v17.no1.p84-93

Keywords:

Water Meter Detection, YOLOv11, Image Augmentation

Abstract

While accurate water management is crucial for public utilities, manual meter reading remains inefficient due to recording errors and high operational costs. This study proposes an automatic water meter reading detection system based on YOLOv11 with a variety of image augmentation techniques integrated into Telegram. The dataset was obtained through a combination of ESP32-CAM image captures and online sources totaling 1,207 images, followed by labeling on Roboflow and augmentation in the form of flipping, rotation, saturation, and noise. The YOLOv11 model was trained on Google Colab using an A100 GPU with 100 epochs. Performance evaluation was conducted using Precision, Recall, mAP50, and mAP50-95 metrics. The results showed that the application of augmentation significantly improved model performance, with Precision of 95.9%, Recall of 98.2%, and mAP50 of 97.4%. The combination of four augmentation techniques produced the highest mAP50-95 value of 0.575, indicating the model's robustness against variations in field conditions. The system is also capable of automatically sending detection result notifications via Telegram, enabling its implementation for real-time remote monitoring. Compared to previous studies with YOLOv4 and YOLOv5, this approach proved to be superior in terms of accuracy and efficiency. These findings indicate that the integration of YOLOv11 with image augmentation techniques and IoT support has the potential to be an optimal solution in the modernization of digital water meter reading.

References

Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint.

Cong, P., Lv, K., Feng, H., & Zhou, J. (2022). Improved YOLOv3 Model for Workpiece Stud Leakage Detection. Electronics, 11(21). https://doi.org/10.3390/electronics11213430 DOI: https://doi.org/10.3390/electronics11213430

Elkharbotly, M. R., Seddik, M., & Khalifa, A. (2022). Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt. Ain Shams Engineering Journal, 13(5), 101673. https://doi.org/https://doi.org/10.1016/j.asej.2021.101673 DOI: https://doi.org/10.1016/j.asej.2021.101673

Gleick, P. H. (1998). The human right to water. Water Policy, 1(5), 487–503. https://doi.org/https://doi.org/10.1016/S1366-7017(99)00008-2 DOI: https://doi.org/10.1016/S1366-7017(99)00008-2

Gupta, A. D., Pandey, P., Feijóo, A., Yaseen, Z. M., & Bokde, N. D. (2020). Smart Water Technology for Efficient Water Resource Management: A Review. Energies, 13(23). https://doi.org/10.3390/en13236268 DOI: https://doi.org/10.3390/en13236268

Hussain, D., Memon, S., Das, L., Salam, D. M., Salam, & Awan, J. (2017). Issues and Challenges of Existing Electricity Pre-Paid Smart Metering Systems: A Review. Science, Technology and Development, 36, 44–52. https://doi.org/10.3923/std.2017.44.52

Javaid, A., Sattar, M. U., & Ilchenko, O. (2025). Efficient Object Detection in Smart Cities Using a Lightweight DETR Model. In 2025 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 1–7). https://doi.org/10.1109/CloudCom67567.2025.11331493 DOI: https://doi.org/10.1109/CloudCom67567.2025.11331493

Jiang, Z., Messner, J. I., & Matts, E. (2023). COMPUTER VISION APPLICATIONS IN CONSTRUCTION AND ASSET MANAGEMENT PHASES : A LITERATURE REVIEW, 28(November 2022), 176–199. https://doi.org/10.36680/j.itcon.2023.009 DOI: https://doi.org/10.36680/j.itcon.2023.009

Kang, S., Hu, Z., Liu, L., Zhang, K., & Cao, Z. (2025). Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis. Electronics, 14(6). https://doi.org/10.3390/electronics14061104 DOI: https://doi.org/10.3390/electronics14061104

Li, J., Shen, J., Nie, K., Du, R., Zhu, J., & Long, H. (2024). Reading Recognition Method of Mechanical Water Meter Based on Convolutional Neural Network in Natural Scenes. Journal of Advanced Computational Intelligence and Intelligent Informatics, 28(1), 206–215. https://doi.org/10.20965/jaciii.2024.p0206 DOI: https://doi.org/10.20965/jaciii.2024.p0206

Mhlanga, M. (2025). Digital Humanities , Social Science and Cultural Preservation The Impact of Investments in Water Supply Infrastructure on Water Scarcity in Lesotho, 1(1), 1–15. DOI: https://doi.org/10.2139/ssrn.5391729

Murat, A. A., & Kiran, M. S. (2025). A comprehensive review on YOLO versions for object detection. Engineering Science and Technology, an International Journal, 70, 102161. https://doi.org/https://doi.org/10.1016/j.jestch.2025.102161 DOI: https://doi.org/10.1016/j.jestch.2025.102161

Naim, A., Aaroud, A., Akodadi, K., & El Hachimi, C. (2021). A fully AI-based system to automate water meter data collection in Morocco country. Array, 10, 100056. https://doi.org/https://doi.org/10.1016/j.array.2021.100056 DOI: https://doi.org/10.1016/j.array.2021.100056

Pambudi, A. S., & Pramujo, B. (2025). Strengthening Water Governance and Regulatory Effectiveness for Equitable Access in Indonesia, 219–231.

Puspita, R., Naufal, M., & Zami, F. (2025). Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection. Journal of Applied Informatics and Computing, 9, 3601–3611. https://doi.org/10.30871/jaic.v9i6.11256 DOI: https://doi.org/10.30871/jaic.v9i6.11256

Ramos, H. M., Kuriqi, A., Besharat, M., Creaco, E., Tasca, E., Coronado-Hernández, O. E., … Iglesias-Rey, P. (2023). Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks. Water, 15(6). https://doi.org/10.3390/w15061129 DOI: https://doi.org/10.3390/w15061129

Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Retrieved from http://arxiv.org/abs/1804.02767

Sidharth, K. A., Srilekha, P., Shyamkumar, M., Boobalan, M., Archana, R., & Visvak, S. (2022). Water Quality Detection using Computer Vision and IoT. In 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) (pp. 1–3). https://doi.org/10.1109/ICPECTS56089.2022.10047091 DOI: https://doi.org/10.1109/ICPECTS56089.2022.10047091

Tan, M., & Le, Q. V. (2021). EfficientNetV2 : Smaller Models and Faster Training.

Tsirtsakis, P., Zacharis, G., Maraslidis, G. S., & Fragulis, G. F. (2025). Deep learning for object recognition: A comprehensive review of models and algorithms. International Journal of Cognitive Computing in Engineering, 6, 298–312. https://doi.org/https://doi.org/10.1016/j.ijcce.2025.01.004 DOI: https://doi.org/10.1016/j.ijcce.2025.01.004

Varghese, R., & M., S. (2024). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (pp. 1–6). https://doi.org/10.1109/ADICS58448.2024.10533619 DOI: https://doi.org/10.1109/ADICS58448.2024.10533619