Katsunori Mizuno

h-index10
2papers
531citations

2 Papers

2.0CVDec 28, 2024
Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis

Xiaoteng Zhou, Katsunori Mizuno

With the development of coastal construction, a large amount of human-generated waste, particularly plastic debris, is continuously entering the ocean, posing a severe threat to marine ecosystems. The key to effectively addressing plastic pollution lies in the ability to autonomously monitor such debris. Currently, marine debris monitoring primarily relies on optical sensors, but these methods are limited in their applicability to underwater and seafloor areas due to low-visibility constraints. The acoustic camera, also known as high-resolution forward-looking sonar (FLS), has demonstrated considerable potential in the autonomous monitoring of marine debris, as they are unaffected by water turbidity and dark environments. The appearance of targets in sonar images changes with variations in the imaging viewpoint, while challenges such as low signal-to-noise ratio, weak textures, and imaging distortions in sonar imagery present significant obstacles to debris monitoring based on prior class labels. This paper proposes an optical flow-based method for marine debris monitoring, aiming to fully utilize the time series information captured by the acoustic camera to enhance the performance of marine debris monitoring without relying on prior category labels of the targets. The proposed method was validated through experiments conducted in a circulating water tank, demonstrating its feasibility and robustness. This approach holds promise for providing novel insights into the spatial and temporal distribution of debris.

2.0CVJun 5, 2024
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries

Xiaoteng Zhou, Katsunori Mizuno, Yilong Zhang

In low-visibility marine environments characterized by turbidity and darkness, acoustic cameras serve as visual sensors capable of generating high-resolution 2D sonar images. However, acoustic camera images are interfered with by complex noise and are difficult to be directly ingested by downstream visual algorithms. This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques, which comprises two principal components: a self-supervised denoising framework and a fine feature-guided block. Additionally, the study explores the relationship between the level of image denoising and the improvement in feature-matching performance. Experimental results show that the proposed denoising strategy can effectively filter acoustic camera images without prior knowledge of the noise model. The denoising process is nearly end-to-end without complex parameter tuning and post-processing. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching.