Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset
Chuhong Wang, Hua Li, Chongyi Li, Huazhong Liu, Xiongxin Tang, Sam Kwong

TL;DR
This paper introduces a new underwater camouflaged instance segmentation dataset and a specialized segmentation network that significantly improves the accuracy of segmenting camouflaged marine organisms in underwater images.
Contribution
The paper presents the first underwater camouflaged instance segmentation dataset and a novel segmentation network with three modules tailored for underwater environments and camouflage challenges.
Findings
UCIS-SAM outperforms existing methods on UCIS4K and public benchmarks.
The proposed modules effectively enhance underwater feature learning and boundary detection.
The dataset provides a valuable resource for future underwater vision research.
Abstract
With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Image Enhancement Techniques · Olfactory and Sensory Function Studies
