Texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection.

The sea-sky line is an important basis for marine unmanned equipment to perceive the sea environment. However, due to the interference of many external factors, such as sea fog, illumination, and sea target occlusion, sea-sky line detection is challenging. Therefore, we present a texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection. In this method, image texture features and weighted texture quantization are used to determine the sea-sky area. Based on this sea-sky area, a longitudinal block strategy is introduced, and a new adaptive Otsu segmentation method is applied to obtain the binary image of the sea-sky area. The obtained binary image is then post-processed to enhance the sea-sky line edge information. On this basis, a new adaptive Canny edge detector is applied, and the sea-sky line is extracted by the Hough transform. The experimental results show that the proposed sea-sky line detection method has high accuracy and robustness when handling images of complex marine environments. Compared with other algorithms, the detection error and the error standard deviation of the proposed method are relatively small, indicating that this method is more stable and accurate than other algorithms. [ABSTRACT FROM AUTHOR]