Abstract: Sea-sky-line detection is of great significance in the security of marine engineering activities. Sea-sky-line detection is susceptible to external interference in real marine environment such as clouds, waves, illumination variation, target occlusions and boundary blur, etc. A sea-sky-line detection algorithm based on L₀ gradient smoothing and image segmentation & clusters is proposed. Firstly, the image is filtered by L₀ gradient smoothing to enhance sea-sky-lines edge and weaken the interference of non-sea-sky-lines. Then, the image is segmented into several equal-width image blocks along vertical direction to reduce environmental interference and strengthen the detection effect of local sea-sky-lines. The straight line segments in each segmented image block are extracted by Canny operator and Hough transform. Finally, K-means clustering algorithm is adopted to extract the sea-sky-line in each image block, thus fitting to generate the final sea-sky-line. Experimental results show that average accuracy of bounding box overlap rate is 93.22% and the average accuracy of angle difference ration is 7.66% in the real sea-sky-line dataset, both of which are higher in comparison with typical algorithms selected in recent years. Result meets the requirements of real sea-sky-line detection with characters of strong anti-interference, high accuracy and wide adaptability.