Object tracking is an important basis for the autonomous navigation of unmanned surface vehicles. However, several problems still must be addressed for a wide applicating of object tracking in unmanned surface vehicles. First, if multiple objects of the same classification exist in the same field of view, then stable extraction of an object is difficult. Second, in an environment with a complex background and large changes in object shape, the tracking accuracy is low, and object tracking errors and tracking loss can easily occur. Third, much time is required to detect a high-resolution real-time video stream, not meeting the delay requirement of the photoelectric servo stable tracking. To resolve these problems, this paper proposes an object detection-tracking algorithm based on a radar-photoelectric system. The algorithm combines an object detection algorithm with an object tracking algorithm and involves the following steps. First, a first-frame object extraction algorithm is used to extract the tracking object from the first frame. Second, a region of interest (ROI)-prediction algorithm is used to predict ROIs and detect objects in these ROIs. This algorithm can effectively solve the above problems in marine tests. When multiple objects of the same classification exist in the same field of view, the algorithm can extract the radar-guided object stably. When faced with a complex background and a large change in object shape, the algorithm substantially improves the accuracy and robustness of object tracking. Compared with the conventional object detection algorithm, the time consumption of this algorithm is reduced by 25.8%.

Unmanned surface vehicles frequently encounter foggy weather when performing surface object tracking tasks, resulting in low optical image quality and object recognition accuracy. Traditional defogging algorithms are time consuming and do not meet real-time requirements. In addition, there are problems with oversaturated colors, low brightness, and overexposed areas in the sky. In order to solve the problems mentioned above, this paper proposes a defogging algorithm for the first frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm involves the following steps. The first is the fog detection algorithm for sea surface image, which determines the presence of fog. The second is the sea-sky line extraction algorithm which realizes the extraction of the sea-sky line in the first frame image. The third is the object detection algorithm based on the sea-sky line, which extracts the target area near the sea-sky line. The fourth is the local defogging algorithm, which defogs the extracted area to obtain higher quality images. This paper effectively solves the problems above in the sea test and dramatically reduces the calculation time of the defogging algorithm by 86.7%, compared with the dark channel prior algorithm.

The radar-photoelectric system is a perception system to detect the surrounding environment based on marine radar and a photoelectric device. Mast obscuration, green water, and multi-object scenes are special scenes that appear in the first-frame image during the navigation of unmanned surface vehicles. The perception system cannot accurately obtain the object information in mast obscuration and green water scenes. The radar-guided object cannot be stably extracted from the first-frame image in multi-object scenes. Therefore, this paper proposes an object extraction algorithm for the first-frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm realizes the field-of-view adaptation to solve the problem that the features of the radar-guided object are incomplete in the first-frame image and improve the detection accuracy of the local features by 16.8%. The algorithm realizes the scene recognition of the first-frame image to improve the robustness of object tracking. In addition, the algorithm achieves the stable extraction of the radar-guided object in multi-object scenes.

This paper is dedicated to solving the environmental perception system problem of unmanned surface vehicles (USVs) experiencing adverse sea conditions and complex mission scenarios. First, the functionalities and characteristics of each subsystem in the USV environmental perception system under different mission scenarios are analyzed, and an efficient and stable environmental perception system is designed. Second, the static and dynamic characteristics of the sea–sky line are investigated, along with the impacts on each subsystem of the environmental perception system when the USV experiences six-degree-of-freedom motion on the sea surface. Based on the above analysis, a sea–sky line detection method based on the radar–electro-optical system is designed. This method utilizes the features of the radar and electro-optical subsystems to redefine the region of interest, effectively suppressing interference from non-sea–sky line edges, thereby improving detection efficiency and accuracy. Furthermore, a sea–sky line-based target detection algorithm is proposed, which confines the search area to the vicinity of the detected sea–sky line, significantly reducing false detections caused by sea clutter and noise. Sea trials demonstrate that the proposed method enhances the accuracy and real-time performance of USV environmental perception. The proposed systematic approach offers a practical solution for improving the robustness of USV environmental perception in complex marine environments. Sea trials have shown that the method improves the effectiveness of target information by 3.61%.

This paper presents research on a complete, closed-loop environmental perception strategy at the system level and on standardized sea trial verification methods for unmanned surface vehicles (USVs) in typical maritime mission scenarios. Existing research on USV perception mostly focuses on optimizing discrete functional algorithms, such as object detection and tracking, with verification performed only in specific scenarios. These works generally lack task-matched perception schemes covering the full operation cycle and corresponding standardized, reproducible verification systems, making them difficult to adapt to the full-cycle execution requirements of real, complex maritime tasks. To address the above issues, this paper proposes a systematic environmental perception strategy for typical USV operation tasks, establishes a complete workflow from object detection to obstacle avoidance perception and decision-making, and designs two sets of standardized sea trial schemes for core tasks. These schemes provide unified specifications and an evaluation benchmark for verifying the real-world performance of USV environmental perception systems. Finally, full-cycle sea trials on a real ship are completed, and the test results verify the engineering practicability of the proposed perception strategy, as well as the reproducibility, standardization, and extensibility of the established test system, with the laser positioning hit rate improved by 82% and 54% under green-water and foggy conditions compared with the conventional miss distance-based method.