A Sea–Sky Line Detection Method Based on the RANSAC Algorithm in the Background of Infrared Sea–Land–Sky Images

Subgrade sea and sky monitoring equipment requires the accurate detection of threat targets in a given area. Due to the extremely complex sea–land–sky backgrounds, the sea–sky line is often submerged in the background. Therefore, we propose an algorithm for accurately detecting sea–sky lines under a complex sea–land–sky background. Based on the analysis of infrared images with sea–land–sky backgrounds, we segment images using the k-means algorithm. To use the random sampling consistency (RANSAC) algorithm to fit the sea–sky line better, we divide the images into nonuniform segments and count the row mean-value gradient trough. The experimental results show that the sea–sky line can be detected in 1215 out of 1227 pictures. The test success rate is 99%, and the difference from the actual sea–sky line is less than 3 pixels. The method presented has higher adaptability under a subgrade sea and sky monitoring environment.