Sea-sky-line detection base on L0 gradient smoothing and image segmentation clusters
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.
marine hydrologysea-sky-line detectionL0 gradient smoothingimage segmentationK-means linear cluster
万方数据
维普
