Cross-Modal Self-Supervised Learning for Efficient Water Leak Localization with Micro Air Vehicles

Efficient water leakage detection is crucial for smart building water management. Conventional methods relying on fixed sensors are costly to install and maintain. Micro air vehicles (MAVs) offer a cost-effective solution for confined spaces but have limited processing power and payload capacity. MAVs are generally equipped with lightweight sensors, such as compact thermal cameras, which produce low-resolution images and typically limit the detection range. To enable effective long-range detection, this paper proposes a novel cross-modal self-supervised learning (SSL) setup, where a computer vision algorithm is designed to identify water leakage from thermal images. These thermal-based detections then guide the training of a deep learning model using color input images. Furthermore, a control strategy integrating the color-based deep learning model with thermal imaging is introduced to enable MAVs to provide preliminary long-range leakage detection and precise localization when approaching affected areas. The system was tested in both static setups and flight scenarios. In flight tests, the MAV successfully identified leakage across multiple trials, beginning from various initial positions and targeting different leakage sites. Results from both static and dynamic tests demonstrate the effectiveness of the control strategy and detection algorithm in localizing water leakage in indoor environments.