Review On Deep Learning Technique For Underwater Object Detection

• URL: http://arxiv.org/abs/2209.10151v1
• Date: Wed, 21 Sep 2022 07:10:44 GMT
• Title: Review On Deep Learning Technique For Underwater Object Detection
• Authors: Radhwan Adnan Dakhil and Ali Retha Hasoon Khayeat
• Abstract summary: Repair and maintenance of underwater structures as well as marine science rely heavily on underwater object detection. This article provides an overview of the dataset that has been utilized in underwater object detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• 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)
• Is Underwater Image Enhancement All Object Detectors Need? [27.909292529992584]
  It is unclear whether all object detectors need underwater image enhancement as pre-processing. We use 18 state-of-the-art underwater image enhancement algorithms to pre-process underwater object detection data. We retrain 7 popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models.
  arXiv  Detail & Related papers  (2023-11-30T18:54:08Z)
• 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)
• Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN [60.257791714663725]
  We propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. The proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.
  arXiv  Detail & Related papers  (2022-12-23T03:00:28Z)
• Self-Supervised Monocular Depth Underwater [8.830479021890575]
  In the past years estimation of depth from monocular images have shown great improvement. In the underwater environment results are still lagging behind due to appearance changes caused by the medium. We suggest several additions to the self-supervised framework to cope with the underwater environment.
  arXiv  Detail & Related papers  (2022-10-06T20:57:58Z)
• A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline [8.326477369707122]
  This paper proposes a novel method for capturing feature information, which adds the convolutional block attention module (CBAM) to the YOLOv5 backbone. The interference of underwater creature characteristics on object characteristics is decreased, and the output of the backbone network to object information is enhanced.
  arXiv  Detail & Related papers  (2022-05-20T14:18:17Z)
• Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
  "Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
  arXiv  Detail & Related papers  (2021-07-07T12:09:04Z)
• Underwater Image Restoration via Contrastive Learning and a Real-world Dataset [59.35766392100753]
  We present a novel method for underwater image restoration based on unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images.
  arXiv  Detail & Related papers  (2021-06-20T16:06:26Z)
• A Benchmark dataset for both underwater image enhancement and underwater object detection [34.25890702670983]
  We provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images. The OUC dataset provides a platform to comprehensive study the influence of underwater image enhancement algorithms on the underwater object detection task.
  arXiv  Detail & Related papers  (2020-06-29T03:12:50Z)

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.