Low Cost AI Based Detection of Floating Objects Using Stereo Cameras and Rada

In this paper, we present a low-cost situational awareness system designed to assist visu ally impaired in navigating constrained maritime environments. The system integrates stereo vision, consisting of two black-and-white cameras and a central color camera, along with radar, to detect and classify obstacles at sea. This setup is a cost-effective alternative to traditional LiDAR systems, lever aging an AI chip that runs ODESSA, a YOLO v9-based model, for real-time obstacle classification. Designed specifically for operation under fair weather conditions, the system captures and processes visual data to identify various types of floating objects, enhancing navigation safety. The stereo vision provides obstacle classification and depth information, while the radar complements the detection range with velocity, heading and azimuth. After calibration and synchronization, a projection-based methods fuse data between camera and radar. The system’s affordability and versatility position it as a potential solution for future development in accessible maritime navigation technologies.