Learning Underwater Active Perception in Simulation

• URL: http://arxiv.org/abs/2504.17817v1
• Date: Wed, 23 Apr 2025 06:48:38 GMT
• Title: Learning Underwater Active Perception in Simulation
• Authors: Alexandre Cardaillac, Donald G. Dansereau,
• Abstract summary: Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures.<n>Previous works have introduced methods to adapt to turbidity and backscattering.<n>We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions.
• Score: 51.205673783866146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When employing underwater vehicles for the autonomous inspection of assets, it is crucial to consider and assess the water conditions. Indeed, they have a significant impact on the visibility, which also affects robotic operations. Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures. Previous works have introduced methods to adapt to turbidity and backscattering, however, they also include manoeuvring and setup constraints. We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions. This active perception framework includes a multi-layer perceptron (MLP) trained to predict image quality given a distance to a target and artificial light intensity. We generated a large synthetic dataset including ten water types with different levels of turbidity and backscattering. For this, we modified the modelling software Blender to better account for the underwater light propagation properties. We validated the approach in simulation and showed significant improvements in visual coverage and quality of imagery compared to traditional approaches. The project code is available on our project page at https://roboticimaging.org/Projects/ActiveUW/.

Related papers

• Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond [25.975859029063585]
  We propose a lightweight phase-based transformer network with 1.77M parameters for underwater image restoration (UIR)<n>Our approach focuses on effectively extracting non-contaminated features using a phase-based self-attention mechanism.<n>We demonstrate its effectiveness for low-light image enhancement using the LOL dataset.
  arXiv  Detail & Related papers  (2024-12-02T12:48:19Z)
• HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning [62.264673293638175]
  Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications.<n>We propose a invertible network for underwater perception enhancement, dubbed H, which enhances visual quality and demonstrates flexibility in handling other downstream tasks.
  arXiv  Detail & Related papers  (2024-11-27T12:37:03Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation [65.01601309903971]
  We introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs) Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-25T03:54:01Z)
• Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method [0.0]
  We have utilized the cycle consistent learning technique of the state-of-the-art Cycle GAN model with modification in the loss function. We trained the Cycle GAN model with the modified loss functions on the benchmarked Enhancing Underwater Visual Perception dataset. The upgraded images provide better results from conventional models and further for under water navigation, pose estimation, saliency prediction, object detection and tracking.
  arXiv  Detail & Related papers  (2024-04-11T11:12:06Z)
• An Efficient Detection and Control System for Underwater Docking using Machine Learning and Realistic Simulation: A Comprehensive Approach [5.039813366558306]
  This work compares different deep-learning architectures to perform underwater docking detection and classification. A Generative Adversarial Network (GAN) is used to do image-to-image translation, converting the Gazebo simulation image into an underwater-looking image. Results show an improvement of 20% in the high turbidity scenarios regardless of the underwater currents.
  arXiv  Detail & Related papers  (2023-11-02T18:10:20Z)
• Adaptive deep learning framework for robust unsupervised underwater image enhancement [3.0516727053033392]
  One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data.<n>We propose a novel unsupervised underwater image enhancement framework that employs a conditional variational autoencoder (cVAE) to train a deep learning model.<n>We show that our proposed framework yields competitive performance compared to other state-of-the-art approaches in quantitative as well as qualitative metrics.
  arXiv  Detail & Related papers  (2022-12-18T01:07:20Z)
• Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement [58.075720488942125]
  Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. We develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model. We also apply the proposed algorithm to the underwater salient object detection task to reveal the favorable semantic-aware ability for high-level vision tasks.
  arXiv  Detail & Related papers  (2022-11-19T07:50:34Z)
• Optical Flow Estimation from a Single Motion-blurred Image [66.2061278123057]
  Motion blur in an image may have practical interests in fundamental computer vision problems. We propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-03-04T12:45:18Z)
• Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement [10.143025577499039]
  We propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2020-02-20T07:50:00Z)

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.