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Secure Indoor Water Level Monitoring with Temporal Super-Resolution and Enhanced Yolov5
Sui Guo1, Jiazhi Huang1, Yuming Yan1
1China Yangtze Power Co., Ltd., Yichang 443000, China.
Sensors (Basel, Switzerland)
|May 14, 2025
Summary
This study presents a new framework for reliable indoor water level monitoring in hydropower plants. It uses super-resolution and an improved Yolov5 model to ensure accuracy in challenging conditions like poor lighting and mist.
Area of Science:
- Engineering
- Computer Science
Background:
- Secure and efficient water level monitoring is crucial for intelligent hydropower plant management.
- Existing methods fail in challenging indoor environments with poor lighting, water vapor, and confined spaces.
Purpose of the Study:
- To develop a robust indoor water level monitoring framework for hydropower plants.
- To overcome limitations of existing methods in suboptimal conditions.
Main Methods:
- Integration of a temporal super-resolution enhancement module for low-resolution image processing.
- Implementation of an improved, lightweight Yolov5 model with a small-scale feature mapping branch for real-time detection.
Main Results:
- The framework achieves high accuracy and reliability in diverse conditions (day, night, mist, wet surfaces).
- Real-time monitoring and accurate detection are enabled by the enhanced Yolov5 model.
- Recognition speeds reach O(n), demonstrating operational efficiency.
Conclusions:
- The proposed framework offers a robust solution for indoor water level monitoring in hydropower plants.
- It provides vital support for security and emergency management in intelligent hydropower systems.
- Suitable for integration with emerging intelligent systems like HT-for-Web analysis software and warning platforms.
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