Abstract
The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.
Highlights
With the rapid development of artificial intelligence technology, unmanned surface vehicles (USVs) have become more and more important in maritime systems [1,2,3]
The vision sensors equipped on the USVs usually are used to perceive surrounding information, which plays an important role in guaranteeing the safety and efficiency of the USVs without human intervention
The pixel points of the horizon line features are far fewer than the pixel points of the entire image; and second, horizon line detection suffers from the complex maritime environment, waves, light changing and partial occlusions by maritime vessels or islands, for example
Summary
With the rapid development of artificial intelligence technology, unmanned surface vehicles (USVs) have become more and more important in maritime systems [1,2,3]. The Hough transform, and intensity gradients are used to find the line feature candidates These methods using local features are effective for line segment extraction, the major shortcoming is that they are unable to distinguish the horizon line from the similar line segments in the sea image. The aim of this paper is to propose a robust maritime horizon line detection method, coarse-fine-stitched (CFS), for USV applications. The first step of CFS is the coarse detection, in which a line segment detection approach in which gradient features are applied to extract all the line segments in the image to build a line candidate pool. There are a few missing detections in the candidate pool, it probably contains many false detection results due to the background environment To solve this problem, using the fine step of CFS, hybrid feature filtering is designed to select the horizon line segments from the pool.
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