Extracting sea-sky-line based on improved local complexity

Dong, Yue-fang; Zhu, Chao; Zhang, Yunfeng; Wang, Anbang

doi:10.1109/cmce.2010.5609912

Cited by 12 publications

(4 citation statements)

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“…There are only a few water surface studies, and most of them are based on the marine environment. Dong [24] and Lan [25] et al proposed an image based local complexity and Hough transform to realize sea-sky-line or sea-land-line detection, but Hough transform needs to balance detection accuracy and calculation speed, in addition, it is also affected by strong edge and noise interference, resulting in clutter and flicker, which usually produces false lines. Gong et al [26] detected the shoreline by constructing a two-stage algorithm with multiple features in the image.…”

Section: River Boundary Line Detectionmentioning

confidence: 99%

“…A very similar task to river boundary detection is shoreline detection, many existing shoreline detection methods are based on image vision [21][22][23][24][25][26], and the detection results are pixel-based semantic segmentation, mainly divided into water surface or non-water surface areas. Image-based river detection algorithms are vulnerable to diversification of river scenes, illumination changes and water surface reflection interference.…”

Section: Introductionmentioning

confidence: 99%

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River boundary detection and autonomous cruise for unmanned surface vehicles

Zhang

Ren

et al. 2023

IET Image Processing

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The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust.

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Section: River Boundary Line Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

River boundary detection and autonomous cruise for unmanned surface vehicles

Zhang

Ren

et al. 2023

IET Image Processing

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“…Then the first-order differential operator with direction is used to locate the maximum value of the derivative [7] , and the non-maximal value of the gradient amplitude is suppressed. The double threshold algorithm [8] is used to detect and connect the edges.…”

Section: Image Preprocessingmentioning

confidence: 99%

To Describe the Complexity of Image Edge Based on the Information Entropy Method

Li¹,

Chen²,

Luo³

et al. 2018

dtetr

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A method of image edge complexity description based on information entropy is proposed. Firstly, the long axis of the object is rotated to the horizontal direction, then the target image is united in size, and then the connected region is computed to obtain the coordinates of the target edge points. The first order difference and the second order difference of the adjacent edge point coordinates are obtained in turn. A number of statistical difference and second order difference take different values, calculating the probability of different value. According to the entropy formula, the first order differential information entropy and the second order differential information entropy of the edge are calculated. This method can avoid the effect of target rotation on calculation results, and can describe the complexity of the target edge very well, and it can be used as one of the identification features of the target.

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“…Rahman, et al accomplished horizon detection with the Canny edge detection and Hough transform methods [ 4 , 5 , 6 , 7 , 8 ], but the Hough transform needs a compromise between detection accuracy and computational complexity, moreover, it suffers from interference of strong edges and noise like cloud clutter and wave glint, and the Hough transform often fabricates false line segments.…”

Section: Introductionmentioning

confidence: 99%

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency

Wang

Wan

2016

Sensors

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Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the “XL” USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

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Extracting sea-sky-line based on improved local complexity

Cited by 12 publications

References 1 publication

River boundary detection and autonomous cruise for unmanned surface vehicles

River boundary detection and autonomous cruise for unmanned surface vehicles

To Describe the Complexity of Image Edge Based on the Information Entropy Method

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency

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