Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis

• URL: http://arxiv.org/abs/2412.20085v1
• Date: Sat, 28 Dec 2024 08:55:37 GMT
• Title: Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis
• Authors: Xiaoteng Zhou, Katsunori Mizuno,
• Abstract summary: The paper proposes an optical flow-based method for marine debris monitoring. The proposed method was validated through experiments conducted in a circulating water tank.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis [0.0]
  This paper conducts a comprehensive review of state-of-the-art architectures and on the existing datasets to establish a baseline for submerged waste and trash detection. The primary goal remains to establish the benchmark of the object localization techniques to be leveraged by advanced underwater sensors and autonomous underwater vehicles.
  arXiv  Detail & Related papers  (2024-05-28T15:51:18Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement [70.2429155741593]
  This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT) It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. We propose a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers.
  arXiv  Detail & Related papers  (2023-08-30T07:41:26Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter [6.198237241838559]
  Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. We analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios.
  arXiv  Detail & Related papers  (2023-07-22T18:59:27Z)
• Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2 [3.6842260407632903]
  Efforts to quantify marine pollution are often conducted with sparse and expensive beach surveys. Satellite data of coastal areas is readily available and can be leveraged to detect aggregations of marine debris containing plastic litter. We present a detector for marine debris built on a deep segmentation model that outputs a probability for marine debris at the pixel level.
  arXiv  Detail & Related papers  (2023-07-05T17:38:48Z)
• GAMMA: Generative Augmentation for Attentive Marine Debris Detection [0.0]
  We propose an efficient and generative augmentation approach to solve the inadequacy concern of underwater debris data for visual detection. We use cycleGAN as a data augmentation technique to convert openly available, abundant data of terrestrial plastic to underwater-style images. We also propose a novel architecture for underwater debris detection using an attention mechanism.
  arXiv  Detail & Related papers  (2022-12-07T16:30:51Z)
• AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models [64.24948495708337]
  Atmospheric turbulence causes significant degradation to image quality by introducing blur and geometric distortion. Various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained some traction because of their stable training process and their ability to generate high quality images.
  arXiv  Detail & Related papers  (2022-08-24T03:13:04Z)
• DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models [0.0]
  Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. The need for physical removal before analysis incurs high costs and requires intensive labor preventing scalable deployment of a real-time marine plastic monitoring service. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input.
  arXiv  Detail & Related papers  (2021-05-05T06:04:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.