L0 Gradient Smoothing and Bimodal Histogram Analysis

Abstract

Sea-sky-line detection is an important research topic in the field of object detection and tracking on the sea. We propose an L0 gradient smoothing and bimodal histogram analysis based method to improve the robustness and accuracy of sea-sky-line detection. The proposed method mainly depends on the brightness difference between the sea region and the sky region in the image. First, we use L0 gradient smoothing to eliminate discrete noise in the image and achieve the modularity of brightness. Differing from previous methods, diagonal dividing is applied to obtain the brightness thresholds for the sky and sea regions. Then the thresholds are used for bimodal histogram analysis which helps to obtain the brightness near the sea-sky-line and narrow the detection region. After narrowing the detection region, the sea-sky-line in the image is extracted by a linear fitting method. To evaluate the performance of the proposed method, we manually construct an dataset which includes 40, 000 images taken in five scenes. Moreover, we also mark the corresponding ground-truth positions of sea-sky-line in each of the images. Extensive experiments on the dataset demonstrate that our method outperforms the state-of-the-art methods tremendously.