Scholarlink

<p>Multimodal devices utilizing optical cameras and LiDAR are crucial for precise underwater environmental perception. Enhancing and optimizing RGB images and laser point clouds through algorithms is a key focus in underwater computer vision. This research necessitates extensive underwater multivariate data for training and evaluation, along with the organization and labeling of this data. To meet these needs, we present a multimodal optical dataset for underwater image detection, segmentation, enhancement, and 3D reconstruction (MOUD). The dataset comprises over 18,000 original RGB images, 12,000 labeled images, 60 point cloud sets, and several labeled point clouds. The labeled images feature nine different target objects, including scallop, starfish, conch, holothuria, seaweed, coral, reef, abalone, and barnacle. These data were gathered from our underwater simulation scenarios. To ensure accuracy and utility, we employed a state-of-the-art image-laser underwater detector with appropriate kinematic parameters. This dataset supports training and evaluation in underwater image enhancement, detection, segmentation, and reconstruction, which are vital for precise underwater sensing. Consequently, this dataset holds significant importance for advancing underwater optical exploration technology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Multimodal Optical Dataset for Underwater Image Enhancement, Detection, Segmentation, and Reconstruction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月25日

查看全文
  • Xuanhe Chu,
  • Han Chen,
  • Dehua Zou,
  • Junwen Tian,
  • Qi Gao,
  • Shijian Zhou,
  • Yancheng Liu,
  • Jianda Wang,
  • Jingchun Zhou,
  • Xianping Fu,
  • Siyuan Liu

摘要

Multimodal devices utilizing optical cameras and LiDAR are crucial for precise underwater environmental perception. Enhancing and optimizing RGB images and laser point clouds through algorithms is a key focus in underwater computer vision. This research necessitates extensive underwater multivariate data for training and evaluation, along with the organization and labeling of this data. To meet these needs, we present a multimodal optical dataset for underwater image detection, segmentation, enhancement, and 3D reconstruction (MOUD). The dataset comprises over 18,000 original RGB images, 12,000 labeled images, 60 point cloud sets, and several labeled point clouds. The labeled images feature nine different target objects, including scallop, starfish, conch, holothuria, seaweed, coral, reef, abalone, and barnacle. These data were gathered from our underwater simulation scenarios. To ensure accuracy and utility, we employed a state-of-the-art image-laser underwater detector with appropriate kinematic parameters. This dataset supports training and evaluation in underwater image enhancement, detection, segmentation, and reconstruction, which are vital for precise underwater sensing. Consequently, this dataset holds significant importance for advancing underwater optical exploration technology.