Improve Real-Time Flood Segmentation by Encoding and Distilling Foreground Information

Flood segmentation systems play a crucial role in natural disaster management, particularly for real-time flood monitoring, thus real-time lightweight deep neural network (DNN) models represent the state-of-the-art (SOTA) solution. A neglected aspect during the design of such solutions is that flood segmentation is a computer vision problem where the variance of visual appearance between the foreground (flood) and the background is imbalanced. This paper mitigates this imbalance using Knowledge Distillation (KD), enhancing the performance of real-time SOTA DNN models for flood segmentation in complex and challenging environments. The proposed method employs a Self-KD approach, where a Teacher model, trained on augmented inputs with reduced background variance by exploiting traditional image processing techniques (e.g., blur-ring), guides a Student model operating on real-world data. By consistently processing augmented inputs, the Teacher model facilitates the Student’s ability to learn robust representations, effectively suppressing noisy background elements. Experimental results on a flood dataset demonstrate an improvement of up to 2.5% in mean Intersection over Union (mIoU) over baseline SOTA models which scored 85% mIoU, highlighting the effectiveness of the proposed method. Furthermore, our approach is model-agnostic, consistently improving the performance of various SOTA DNN architectures across different models.