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

Sensors ◽  
2016 ◽  
Vol 16 (4) ◽  
pp. 543 ◽  
Author(s):  
Bo Wang ◽  
Yumin Su ◽  
Lei Wan
Keyword(s):  
Detection Method ◽  
Line Detection ◽  
Unmanned Surface Vehicles

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

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.

Vehicles Categorization in Complex Background

2014 ◽  
Vol 998-999 ◽  
pp. 708-711
Author(s):  
Ying Zhuo Xiang ◽  
Dong Mei Yang ◽  
Ji Kun Yan
Keyword(s):  
Detection Method ◽  
Line Length ◽  
Line Detection ◽  
View Point ◽  
Training Set ◽  
Large Variability ◽  
Linear Classifier ◽  
Complex Background ◽  
Novel Approach ◽  
Dimension Characteristic

This paper presents a novel approach to categorize multi-view vehicles in complex background using only two dimension characteristic vectors instead of high dimension vectors. Vehicles have large variability of models and the view-point makes the appearance change dramatically. Significant characteristics should be chosen as the evidence to categorize. In this paper, we categorize the vehicles into two categories – cars and lorries. Line detection method is used and calculating the average line length and the number of parallel lines as the two characteristics. A linear classifier is trained using 30 different view cars and lorries as the training set and an 10 additional different cars and lorries as the testing set.

scholarly journals EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

2021 ◽  
Vol 11 (4) ◽  
pp. 22-27
Author(s):  
Paweł Kowalski ◽  
Piotr Tojza
Keyword(s):  
Detection Method ◽  
Computational Cost ◽  
New Method ◽  
Line Detection ◽  
Curve Detection ◽  
Real Time Image ◽  
Convolution Filter ◽  
Straight Lines ◽  
Time Image ◽  
Local Detection

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

Chord-line detection method of optical performance for medical rigid endoscopes

2014 ◽  
Vol 22 (3) ◽  
pp. 576-581
Author(s):  
杨冬 YANG Dong ◽  
侯纯伟 HOU Chun-wei ◽  
孙志辉 SUN Zhi-hui ◽  
姬军 JI Jun ◽  
李咏雪 LI Yong-xue
Keyword(s):  
Detection Method ◽  
Line Detection ◽  
Optical Performance
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