The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology for the purpose of detecting impediments that exist on water surfaces. However, it is worth noting that these approaches lack the capability to accurately discern the precise nature or classification of those obstacles. Nevertheless, the limited optical range of unmanned vessels hinders their ability to comprehensively perceive the entirety of the surrounding information. A cooperative USV-UAV system is proposed to ensure the visual perception ability of USVs. The multi-object recognition, semantic segmentation, and obstacle ranging through USV and unmanned aerial vehicle (UAV) perspectives are selected to validate the performance of a cooperative USV-UAV system. The you only look once-X (YOLOX) model, the proportional–integral–derivative-NET (PIDNet) model, and distance measurements based on a monocular camera are utilized to realize these problems. The results indicate that by integrating the viewpoints of USVs and UAVs, a collaborative USV-UAV system, employing the aforementioned methods, can successfully detect and classify different objects surrounding the USV. Additionally, it can differentiate between navigable and non-navigable regions for unmanned vessels through visual recognition, while accurately determining the distance between the USV and obstacles.

Over the past few decades, unmanned surface vehicles (USV) have drawn a lot of attention. But because of the wind, waves, currents, and other sporadic disturbances, it is challenging to understand and collect correct data about USV dynamics. In this paper, the Modified backpropagation neural network (BPNN) is suggested to address this issue. The experiment was conducted in the Qinghuai River, and the receiver collected the data. The modified BPNN outperforms the conventional BPNN in terms of ship trajectory forecasting and the rate of convergence. The updated BPNN can accurately predict the rotational velocity during the propeller’s acceleration and stability stages at various rpms.