Moving Object Detection in Shallow Underwater using Multi-Scale Spatial-Temporal Lacunarity

In shallow underwater environments, active sonar is often utilized for the detection of small moving targets. However, such systems can suffer from high-level background reverberation, making it challenging to detect and distinguish the target echoes from reverberation. To address this challenge, we propose a fast moving object detection method utilizing multi-scale spatial-temporal lacunarity. Specifically, computing lacunarity solely from temporal or spatial dimensions makes it difficult to distinguish between target and reverberation patterns. Our method overcomes this by characterizing both the static and dynamic patterns of target echoes and background reverberation through spatial-temporal lacunarity. Additionally, we introduce an echo pyramid that enables multi-scale observation while reducing computational complexity. Experimental results demonstrate that our proposed method significantly improves the detection of small moving targets from high-level background reverberation. Furthermore, our method outperforms existing methods in terms of visual quality and quantitative metrics.