Sea-sky line detection in the infrared image based on the vertical grayscale distribution feature

When detecting sea-sky line (SSL) in the infrared image, the blurry SSL, conspicuous sea clutter affects the accurate detection of SSL seriously. To solve these problems, we proposed a robust SSL detection algorithm based on the vertical grayscale distribution feature (VGDF). We divided the infrared image into sub-image blocks by sliding window. The sub-image blocks that contain SSL in the central area are labeled as positive samples, and those without any SSL are labeled as negative samples. To improve the separability of the samples, the vertical grayscale distribution feature map (VGDF map) transformation method is proposed to transform the gray sub-image blocks into the feature maps. The VGDF maps are used as the input of the convolutional neural network to train the SSL recognition model. This strategy can improve the separability of SSL image blocks from background image blocks. Then, we use the trained model to obtain the edge candidates and construct the SSL probability feature map. Finally, we detect the SSL by fitting a straight line with the greatest probability on the SSL probability feature map. The proposed algorithm realized 99.4% accuracy rate on the dataset containing 1320 frames of infrared images. The comparison results showed that our algorithm obtained higher detection accuracy than the existing state-of-the-art algorithms. Our algorithm performs well even when the SSL was blurred or there are obvious ship’s wave wakes on the sea surface.