融合图像处理与深度学习，提升水下机器人深度估计精度
Enhancing Depth Image Estimation for Underwater Robots by Combining Image Processing and Machine Learning
- 先用色彩补偿等方法增强水下图像质量
- 在优化后的图像上使用Udepth模型进行深度估计
- 适用于低光浑浊环境下的水下自主导航
深度信息在自主系统环境感知和机器人状态估计中至关重要。随着深度神经网络技术的快速发展，深度估计已得到广泛研究并展现出实际应用潜力。然而，在低光照、高噪声的水下等恶劣环境下，直接应用机器学习模型难以获得理想效果。为此，本文提出一种提升水下图像质量以增强深度估计性能的方法：首先通过色彩校正、亮度均衡以及对比度和锐度增强等图像处理手段改善原始图像；随后在优化后的图像上使用Udepth模型进行深度估计；最后通过实验验证该方法在提升水下图像质量与深度估计准确性方面的有效性，为水下机器人在复杂环境中的感知能力提供支持。
原文摘要 · Abstract (English)
Depth information plays a crucial role in autonomous systems for environmental perception and robot state estimation. With the rapid development of deep neural network technology, depth estimation has been extensively studied and shown potential for practical applications. However, in particularly challenging environments such as low-light and noisy underwater conditions, direct application of machine learning models may not yield the desired results. Therefore, in this paper, we present an approach to enhance underwater image quality to improve depth estimation effectiveness. First, underwater images are processed through methods such as color compensation, brightness equalization, and enhancement of contrast and sharpness of objects in the image. Next, we perform depth estimation using the Udepth model on the enhanced images. Finally, the results are evaluated and presented to verify the effectiveness and accuracy of the enhanced depth image quality approach for underwater robots.
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