Rough-Sea-Horizon-Line Detection using a Novel Color Clustering and Least Squares Regression Method

Muhammad Umair, Manzoor Ahmed Hashmani, Horio Keiichi


Sea horizon line (SHL) detection is the first step in maritime image processing. It is aimed at object detection, navigation of autonomous aerial and sea surface vehicles, and distance estimation. Many methods have been proposed to detect SHL, however, their focus remains on SHL detection in calm sea conditions. For this reason, this study is designed to fill this gap and investigate an efficient method to detect a sea horizon line under rough sea conditions. A novel color clustering and least-squares regression-based method is proposed to solve the issue. Minimizing computational cost, our method identifies the candidate region of interest (CROI) from grayscale image ROIs by analyzing the modality of its pixel intensity histogram. It then applies k-mean clustering to highlight potential sea-sky regions. The regional boundary pixel coordinates are used to construct a horizon line by applying the least-squares regression method. The results of the proposed method were compared to the Canny Edge and Hough Transform (CEHT) method. The gale state sea images were used to test the efficiency of both methods, correctly detecting the starting and ending coordinates of a horizon line and its slope. The results highlight the superiority of the proposed method over the CEHT method. On average, the proposed method identified the horizon line within one degree of error as opposed to CEHT method with the average of five degrees. In half of the images, the error in detecting the horizon starting and ending coordinates for the proposed method was within five or fewer pixels. The overall results show the superiority of the proposed method over the CEHT method in rough sea conditions. The novelty of this study is two-fold. Firstly, it is a pioneering study that proposes a novel method to detect SHL under rough sea conditions. Secondly, the proposed method yielded superior SHL detection results compared to its peers.



Keywords: Sea Horizon Line Detection, Rough Sea Conditions, Candidate Region of Interest, Color Clustering, Least Squares Regression.



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