To address the limitations of existing perception methods for nighttime encounter situation recognition of unmanned surface vessels (USVs), this study proposes an image-based method for navigation-light recognition and encounter situation recognition. In accordance with the International Regulations for Preventing Collisions at Sea (COLREGs), a parameterized 3D geometric model of vessel navigation lights and encounter scenario models is established. Based on the camera imaging principle, a dataset of navigation-light images under various encounter situations is generated through simulation experiments. By analyzing the variation patterns of navigation-light images in different encounter situations, a feature vector composed of area-domain and azimuth-domain features is constructed, and an encounter situation recognition method is developed accordingly. To mitigate the effects of water reflections and interfering light sources in real images, a navigation-light image-processing method is designed for the stable extraction of feature parameters. Simulation results show that the classification accuracy ranges from 96.6% to 98.3% at different distance conditions. In field experiments conducted with a small USV under a three-light configuration, the proposed method achieves a navigation-light recognition accuracy of 96.2% and an encounter situation recognition accuracy of 94.94%. The proposed method provides an interpretable and lightweight complementary visual solution for nighttime encounter situation recognition, complementing existing nighttime perception technologies.
Huang et al. (Tue,) studied this question.
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