Multi-Spectral Imaging and Data Fusion for Real-Time Bleeding Detection

In this paper, we introduce a multi-spectral computer vision system that integrates long-wave infrared (thermal) and near-infrared imaging for real-time bleeding detection. Our methodology leverages deep learning techniques for robust object detection within each imaging modality. Detecting liquids in vision-based systems is particularly challenging due to their irregular shapes and varying intensity patterns. We address these challenges through extensive experimentation. A key aspect of our approach is the development of an optimized annotation strategy that accounts for the thermal properties of liquid patches, improving model training and reducing false positives. Additionally, we analyze how the nature of the surface beneath the liquid, whether soft or solid, influences detection accuracy. To enhance detection consistency, we introduce a mechanism for aligning and matching detected regions across both imaging modalities, which is crucial for distinguishing blood from other warm liquids. Our findings indicate that reducing the number of bounding boxes, by enclosing entire liquid patches rather than segmenting individual droplets, improves detection reliability. Furthermore, we establish an integrated decision-making framework that supports data fusion among detected regions of interest across multiple modalities. In a specific use case, this framework is designed to activate an alarm in the event of bleeding. Rigorous experimental validation demonstrates the effectiveness of the proposed system in accurately detecting blood and differentiating it from other liquid spills across various practical applications.