An obstacle detection approach of transmission lines based on contour view synthesis

2010 ◽  
Author(s):  
Gang Yao ◽  
Yong Liu ◽  
Fangmin Dong ◽  
Bangjun Lei
Keyword(s):  
Transmission Lines ◽  
Obstacle Detection ◽  
View Synthesis ◽  
Detection Approach

Modal Current and Cumulative Sum Based Fault Detection Approach in Transmission Lines

2018 ◽  
Vol 19 (6) ◽  
Author(s):  
Mohammad Amin Jarrahi ◽  
Haidar Samet
Keyword(s):  
Fault Detection ◽  
Transmission Lines ◽  
Fault Analysis ◽  
Cumulative Sum ◽  
Efficiency Assessment ◽  
Fast Approach ◽  
Detection Approach ◽  
Three Phase ◽  
Different Types ◽  
Types Of Faults

AbstractIn this paper, a simple and fast approach is suggested for fault detection in transmission lines. The proposed technique utilizes a modified cumulative sum approach for a modal current to identify faults. The modal current is derived by proper linear mixing of three-phase currents. Since different types of faults may occur in transmission lines, all three-phase currents should be considered during fault analysis. By converting three-phase currents to a modal current, the processing time is reduced and less memory is needed. In this paper, a modal current is processed instead of three-phase currents. The modified cumulative sum approach presented in this paper is capable of decreasing computational burdens on the digital relay and accelerating the fault detection procedure. The proposed fault detection technique is evaluated in four different systems. Moreover, some real recorded field data were deliberated in the efficiency assessment of the proposed method. The results denote high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods from different aspects.

scholarly journals Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6861
Author(s):  
Marius Dulău ◽  
Florin Oniga
Keyword(s):  
Computational Complexity ◽  
Obstacle Detection ◽  
Benchmark Dataset ◽  
Processing Stage ◽  
Detection Approach ◽  
Lidar Measurements ◽  
Ground Point ◽  
Clustering Approach ◽  
Point Detection

In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

Follow-Based Forward Obstacle Detection Using Vision Insensitive Feature for Road Cycling

2013 ◽  
Vol 718-720 ◽  
pp. 2427-2431
Author(s):  
Jing Yang ◽  
Ming Gou
Keyword(s):  
Object Detection ◽  
Image Sequences ◽  
Obstacle Detection ◽  
Road Surface ◽  
Image Subtraction ◽  
Robust Detection ◽  
The Past ◽  
Road Cycling ◽  
Detection Approach ◽  
Road Surfaces

Paper proposes a method for detecting general obstacles on a road by subtracting present and past in road cycling camera images. The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning based methods detect only specific obstacles. To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-road cycling camera image sequences, and then registries road surfaces between the frames. Finally, obstacles are detected by applying image subtraction to the redistricted road surface regions with a vision insensitive feature for robust detection. Experiments were conducted by using several image sequences captured by an actual in-road cycling camera to confirm the effectiveness of the proposed method. The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even with different situations.

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