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.

Sciaenidae is one of the largest fish families, but the phylogeny and taxonomy of these fishes are still being disputed. Furthermore, the taxonomic status of the Chinese bahaba (Bahaba taipingensis), which is an endemic species to China, had never been studied through molecular method. In this study, phylogenetic relationships among sciaenid species were reconstructed using DNA sequence data from the mitochondrial cytochrome b (cyt b) gene and the 16S ribosomal RNA (16S rRNA) gene through Bayesian inference (BI) analyses. The phylogenetic trees indicated that the Chinese bahaba closely related to Collichthys and Pseudosciaena. Previous studies (Meng et al. 2004. Prog Nat Sci 14(5):514–521, [in Chinese]) showed that the subfamily Pseudosciaeninae represented the latest evolutionary sort, which was more suitable with the current environment. Based on our 16S rRNA, the Chinese bahaba showed close relationship with Pseudosciaena, thus the divergence of the Chinese bahaba maybe also very late within the family Sciaenidae. It was probably that the oceanographic or ecological discontinuities of these species vary considerably causing particularly strong breaks. Furthermore, combined with previous studies, we suggested that there was only one genus, Otolithes, within the subfamily Otolithinae. Nevertheless, the taxonomic status of the Chinese bahaba and the phylogeny of the whole family were not completely solved. Additional samples of more species are required to develop a clearer picture of the evolutionary history of Sciaenidae.

In recent years, unmanned surface vehicles (USVs) have gained increasing attention in industry due to their efficiency and versatility in marine operations. Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. Not to mention that actual navigations may involve other dynamic traffic participants. In this work, an improved APF algorithm integrating the boundary potential field method and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. By incorporating the boundary potential field method, this novel approach effectively reduces the computational burden caused by clusters of land obstacles in complex environments, significantly improving computational efficiency. Furthermore, the APF method is refined to ensure the algorithm strictly adheres to COLREGs in head-on, overtaking, and crossing encounters, generating smooth and safe collision avoidance paths. The proposed method was tested in numerous complex scenarios derived from electronic navigational charts. The simulation results demonstrated the robustness and efficiency of the proposed algorithm for collision avoidance within complex maritime environments, providing reliable technical support for autonomous obstacle avoidance in dynamic ocean conditions.