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Accurate and fast sea horizon detection is vital for tasks in autonomousnavigation and maritime security, such as video stabilization, target regionreduction, precise tracking, and obstacle avoidance. This paper introduces anovel sea horizon detector from RGB videos, focusing on rapid and effective seanoise suppression while preserving weak horizon edges. Line fitting methods aresubsequently employed on filtered edges for horizon detection. We address thefiltering problem by extracting line segments with a very low edge threshold,ensuring the detection of line segments even in low-contrast horizonconditions. We show that horizon line segments have simple and relevantproperties in RGB images, which we exploit to suppress noisy segments. Then weuse the surviving segments to construct a filtered edge map and infer thehorizon from the filtered edges. We propose a careful incorporation of temporalinformation for horizon inference and experimentally show its effectiveness. Weaddress the computational constraint by providing a vectorized implementationfor efficient CPU execution, and leveraging image downsizing with minimal lossof accuracy on the original size. Moreover, we contribute a public horizon linedataset to enrich existing data resources. Our algorithm's performance isrigorously evaluated against state-of-the-art methods, and its components arevalidated through ablation experiments. Source code and dataset files areavailable at:

A fast horizon detector and a new annotated dataset for maritime video processing