Abstract: Sea-sky-line detection is a key technology for the visual perception of intelligent navigation of ships.To address the challenge of sea-sky line detection being interfered with by ship,waves,and weather in maritime traffic images,a detection method based on semantic segmentation and Hough Transform is proposed.The image is input into the Mo-SegNet image semantic segmentation algorithm improved based on SegNet network for training,and the image features are learned;the Canny detection algorithm is used to detect the contours in the semantically segmented image;and the Hough Transform is used to fit the edge pixels to get the sea-sky-line detection results.In Scene 1,its detection accuracy and intersection and concatenation ratio reach 99.28% and 74.85%,respectively;in Scene 2,its detection IoU（Intersection over Union）reach 98.60% and 59.41%,respectively;in Scene 3,its detection IoU reach 99.31% and 77.19%,respectively,and the experimental results show that the proposed algorithm combining the semantic segmentation and the Hoff Transform has high accuracy and high precision in the sea-sky-line detection.with high accuracy and effectiveness.