针对水下环境复杂性，提升机器人视觉检测的鲁棒性
Robust Object Detection of Underwater Robot based on Domain Generalization
- 基于领域泛化方法，增强模型对水下多变环境的适应能力
- 在多个水下数据集上实现比传统方法更高的检测精度和稳定性
- 适合水下机器人、海洋监测等实际应用，尤其适用于光照变化大的场景
目标检测旨在获取图像中特定物体的位置与类别，包含分类与定位两项任务。近年来，研究者将目标检测应用于配备视觉系统的水下机器人，以完成海产品捕捞、鱼类养殖、生物多样性监测等任务。然而，水下环境的多样性和复杂性带来了新挑战：首先，水生生物常聚集，导致严重遮挡；其次，生物体善于隐藏，颜色与背景相似；第三，水质差异及光照条件变化剧烈，导致相机拍摄图像失真、对比度低、呈蓝绿色，引发领域偏移，而深度模型通常对领域偏移敏感；第四，水下机器人运动导致图像模糊，水体浑浊，降低能见度。本文针对上述水下环境问题，旨在设计高性能且鲁棒的水下目标检测器。
原文摘要 · Abstract (English)
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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