Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement

This work presents a depth image refinement technique designed to enhance the usability of a commercial camera in underwater environments. Stereo vision-based depth cameras offer dense data that is well-suited for accurate environmental understanding. However, light attenuation in water introduces challenges such as missing regions, outliers, and noise in the captured depth images, which can degrade performance in computer vision tasks. Using the Intel RealSense D455 camera, we captured data in a controlled water tank and proposed a refinement technique leveraging the state-of-the-art Depth-Anything model. Our approach involves first capturing a depth image with the Intel RealSense camera and generating a relative depth image using the Depth-Anything model based on the recorded color image. We then apply a mapping between the Depth-Anything generated relative depth data and the RealSense depth image to produce a visually appealing and accurate depth image. Our results demonstrate that this technique enables precise depth measurement at distances of up to 1.2 meters underwater.