RECORD
Robust Object Detection of Underwater Robot based on Domain Generalization
Computer Vision and Pattern Recognition · Submitted 18 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This paper investigates the problems brought by the underwater environment mentioned above, and aims to design a high-performance and robust underwater object detector.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and location. In recent years, researchers tend to apply object detection to underwater robots equipped with vision systems to complete tasks including seafood fishing, fish farming, biodiversity monitoring and so on. However, the diversity and complexity of underwater environments bring new challenges to object detection. First, aquatic organisms tend to live together, which leads to severe occlusion. Second, theaquatic organisms are good at hiding themselves, which have a similar color to the background. Third, the various water quality and changeable and extreme lighting conditions lead to the distorted, low contrast, blue or green images obtained by the underwater camera, resulting in domain shift. And the deep model is generally vulnerable to facing domain shift. Fourth, the movement of the underwater robot leads to the blur of the captured image and makes the water muddy, which results in low visibility of the water. This paper investigates the problems brought by the underwater environment mentioned above, and aims to design a high-performance and robust underwater object detector.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and location. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | In recent years, researchers tend to apply object detection to underwater robots equipped with vision systems to complete tasks including seafood fishing, fish farming, biodiversity monitoring and so on. | background 1.00 | 0.05 | 0.02 | asserted 0.97 |
| s2 | However, the diversity and complexity of underwater environments bring new challenges to object detection. | background 0.98 | 0.07 | 0.02 | asserted 1.00 |
| s3 | First, aquatic organisms tend to live together, which leads to severe occlusion. | background 1.00 | 0.09 | 0.01 | asserted 0.98 |
| s4 | Second, theaquatic organisms are good at hiding themselves, which have a similar color to the background. | background 1.00 | 0.07 | 0.01 | asserted 1.00 |
| s5 | Third, the various water quality and changeable and extreme lighting conditions lead to the distorted, low contrast, blue or green images obtained by the underwater camera, resulting in domain shift. | background 1.00 | 0.09 | 0.02 | asserted 1.00 |
| s6 | And the deep model is generally vulnerable to facing domain shift. | background 0.98 | 0.06 | 0.02 | asserted 0.96 |
| s7 | Fourth, the movement of the underwater robot leads to the blur of the captured image and makes the water muddy, which results in low visibility of the water. | background 1.00 | 0.17 | 0.02 | asserted 1.00 |
| s8 | This paper investigates the problems brought by the underwater environment mentioned above, and aims to design a high-performance and robust underwater object detector. | contribution 0.99 | 0.40 | 0.12 | asserted 0.81 |
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The paper
Original abstract
Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and location. In recent years, researchers tend to apply object detection to underwater robots equipped with vision systems to complete tasks including seafood fishing, fish farming, biodiversity monitoring and so on. However, the diversity and complexity of underwater environments bring new challenges to object detection. First, aquatic organisms tend to live together, which leads to severe occlusion. Second, theaquatic organisms are good at hiding themselves, which have a similar color to the background. Third, the various water quality and changeable and extreme lighting conditions lead to the distorted, low contrast, blue or green images obtained by the underwater camera, resulting in domain shift. And the deep model is generally vulnerable to facing domain shift. Fourth, the movement of the underwater robot leads to the blur of the captured image and makes the water muddy, which results in low visibility of the water. This paper investigates the problems brought by the underwater environment mentioned above, and aims to design a high-performance and robust underwater object detector.
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