Abstract
Abstract
Accurate near-field underwater visual perception plays a crucial role in marine ecological monitoring and benthic resource exploration. However, the superimposition of the biomimetic characteristics of benthic organisms and the optical degradation caused by the water medium frequently induces significant underwater visual camouflage phenomena. This causes the target foreground and the background to become highly fused in terms of color, texture, and structure, thereby severely limiting the performance of underwater vision algorithms in core tasks such as object detection, image segmentation, and 3D reconstruction. Existing public datasets predominantly focus on salient targets in clear water or under simple backgrounds, lacking comprehensive multi-task benchmarks explicitly tailored for visual camouflage scenarios. To address this gap, we constructed an Underwater Camouflaged Object Dataset (UCOD) for near-field benthic organisms, designed for multi-task analysis to jointly support image enhancement, object detection, pixel-level segmentation, and 3D scene reconstruction. The dataset comprises 7,000 high-resolution RGB images, including 3,500 images with detection annotations, 3,500 images with segmentation masks, and 16 reconstruction sequence folders for 3D reconstruction. It covers six categories of benthic organisms exhibiting typical camouflage characteristics: scallops, fish, conches, abalones, starfish, and sea cucumbers. During data acquisition, calibrated underwater imaging equipment was employed, and the kinematic parameters of the acquisition platform were controlled to improve the stability and spatial consistency of the collected data. The dataset provides useful training data and an evaluation basis for underwater multi-task perception research under visual camouflage conditions, while offering complementary data support for studies on underwater optical sensing and intelligent exploration.