Water Boundary Line Detection for Unmanned Surface Vehicles

2020 ◽  
Vol 13 (8) ◽  
pp. 1145-1152
Author(s):  
Yuejun Liu ◽  
Liyong Ma ◽  
Wei Xie ◽  
Xiaolei Zhang ◽  
Yong Zhang
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Line Detection ◽  
Boundary Line ◽  
Intelligent Environment ◽  
High Brightness ◽  
Unmanned Surface Vehicles ◽  
Environment Perception ◽  
Occurrence Matrix

Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV. Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection. Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas. Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot

scholarly journals Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3096 ◽  
Author(s):  
Junfeng Xin ◽  
Shixin Li ◽  
Jinlu Sheng ◽  
Yongbo Zhang ◽  
Ying Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Navigation ◽  
Particle Swarm ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Surface Vehicles ◽  
Inertia Weight

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

A Real-Time Sea-Sky-Line Detection Method under Complicated Sea-Sky Background

2012 ◽  
Vol 182-183 ◽  
pp. 1826-1831 ◽  
Author(s):  
Yang Gui ◽  
Xiao Hu Zhang ◽  
Yang Shang ◽  
Kun Peng Wang
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Computational Cost ◽  
Input Image ◽  
Line Detection ◽  
Image Position ◽  
Interior Points ◽  
Line Fitting ◽  
Ransac Algorithm ◽  
Correlation Matching

A real-time sea-sky-line detection method under complicated sea-sky background is presented. Firstly, a black-white template is constructed and used for fast correlation matching in several searching regions which are predefined in input image, position of maximal correlation coefficient in each predefined region is hunt out, and coordinates of several candidate sea-sky-line points are made certain according to the position. Then, RANSAC algorithm is applied to preserve interior points which are really on the sea-sky-line and eliminate exterior points which are not. Finally, line parameters of the sea-sky-line can be gained by applying least squares line fitting for all interior points. The pixels of several regions in the image instead of the whole image need to be considered, so computational cost can be reduced dramatically. The experimental results show that the proposed method can detect out sea-sky-line under complicated sea-sky background effectively and has many advantages such as strong robustness and speedy calculation.

scholarly journals A sea-sky-line detection method based on Gaussian mixture models and image texture features

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989211
Author(s):  
Wanhui Yang ◽  
Hengyu Li ◽  
Jingyi Liu ◽  
Shaorong Xie ◽  
Jun Luo
Keyword(s):  
Gaussian Mixture ◽  
Obstacle Detection ◽  
Distribution Model ◽  
Line Detection ◽  
Detection Accuracy ◽  
Gray Level ◽  
Data Set ◽  
Unmanned Surface Vehicles ◽  
Public Data ◽  
Occurrence Matrix

This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.

scholarly journals Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2825 ◽  
Author(s):  
Yuan Sun ◽  
Li Fu
Keyword(s):  
Autonomous Navigation ◽  
Detection Method ◽  
Real Data ◽  
Obstacle Detection ◽  
Line Detection ◽  
Line Segments ◽  
Horizon Line ◽  
The One ◽  
Horizon Line Detection ◽  
Maritime Environment

The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.

Development and Application of Quality Control System for Highway Subgrade Construction

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Tong Wang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Detection Method ◽  
System Development ◽  
Effective Control ◽  
Construction Process ◽  
Development Mode ◽  
The Road ◽  
Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

Pendeteksian Ruang Kosong Parkir di dalam Ruangan

2018 ◽  
Vol 10 (1) ◽  
pp. 34-40
Author(s):  
Nunik Afriliana ◽  
Rosalina Rosalina ◽  
Regina Valeria
Keyword(s):  
Real Time ◽  
Line Detection ◽  
Cascade Classifier ◽  
Index Terms

Menemukan tempat parkir kosong di tempat parkir dalam ruangan seperti pusat perbelanjaan menjadi kesulitan banyak pengemudi, terutama saat jam sibuk di kota-kota besar. Dalam makalah ini, sebuah sistem deteksi kekosongan tempat parkir dalam ruangan diusulkan, dengan menggunakan sistem kamera yang melibatkan OpenCV untuk mempercepat waktu dalam mencari tempat parkir bagi pengemudi kendaraan dengan memberi mereka informasi lokasi dan tempat parkir. Sistem ini menggunakan metode deteksi objek statis, yaitu Haar-Like Cascade Classifier yang dikombinasikan dengan Hough Line Detection untuk mengidentifikasi area parkir kosong dari gambar parkir yang diambil secara real time melalui kamera IP atau kamera USB. Sistem ini dirancang untuk disematkan dengan sistem manajemen parkir sebuah bangunan sebagai alat yang menyediakan tempat parkir untuk membantu pengemudi kendaraan memasuki area parkir. Index Terms—Haar-Like Cascade Classifier, Hough Line Detection, Sistem Maanajemen Parkir

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

scholarly journals A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinkang Wang ◽  
Xiaohui He ◽  
Shao Faming ◽  
Guanlin Lu ◽  
Hu Cong ◽  
...  
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Detection Method
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