  Accurate and fast sea horizon detection is vital for tasks in autonomous
navigation and maritime security, such as video stabilization, target region
reduction, precise tracking, and obstacle avoidance. This paper introduces a
novel sea horizon detector from RGB videos, focusing on rapid and effective sea
noise suppression while preserving weak horizon edges. Line fitting methods are
subsequently employed on filtered edges for horizon detection. We address the
filtering problem by extracting line segments with a very low edge threshold,
ensuring the detection of line segments even in low-contrast horizon
conditions. We show that horizon line segments have simple and relevant
properties in RGB images, which we exploit to suppress noisy segments. Then we
use the surviving segments to construct a filtered edge map and infer the
horizon from the filtered edges. We propose a careful incorporation of temporal
information for horizon inference and experimentally show its effectiveness. We
address the computational constraint by providing a vectorized implementation
for efficient CPU execution, and leveraging image downsizing with minimal loss
of accuracy on the original size. Moreover, we contribute a public horizon line
dataset to enrich existing data resources. Our algorithm's performance is
rigorously evaluated against state-of-the-art methods, and its components are
validated through ablation experiments. Source code and dataset files are
available at:
