Obstacle detection method based on water reflection removal

The safe navigation of unmanned surface vessels is inextricably linked to advanced obstacle detection technology. This technology serves as a critical mechanism, enabling the vessels to timely detect and navigate around obstacles, thereby guaranteeing their safety throughout the voyage. To address the issue of inaccurate water surface segmentation and false obstacle detection due to reflection regions, a novel obstacle detection approach is proposed. This method leverages an enhanced semantic segmentation model to effectively tackle these challenges. This approach encompasses horizon detection, reflection region identification and elimination, and obstacle detection utilizing an enhanced semantic segmentation model. Firstly, the approximate water surface area and horizon parameters are determined by horizon detection. Then, the reflection regions on the water surface are detected and eliminated. Finally, the horizon parameter is integrated into the semantic segmentation model, where the initialization and prior information of the model are adjusted, and the super-prior parameter is attenuated. This optimization enhances the speed and precision of water edge and surface obstacles detection. The experimental results show that compared with other methods, the proposed method has the lowest error in water edge detection, the highest accuracy, recall rate and F-score in obstacle detection, and the lowest average number of false positives (αFP) per frame.