scholarly journals A Simple Outdoor Environment Obstacle Detection Method Based on Information Fusion of Depth and Infrared

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yaguang Zhu ◽  
Baomin Yi ◽  
Tong Guo
Keyword(s):  
Edge Detection ◽  
Information Fusion ◽  
Recognition Rate ◽  
Infrared Image ◽  
Depth Map ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Intelligent Vehicles ◽  
Outdoor Environment ◽  
Depth Information

In allusion to the existing low recognition rate and robustness problem in obstacle detection; a simple but effective obstacle detection algorithm of information fusion in the depth and infrared is put forward. The scenario is segmented by the mean-shift algorithm and the pixel gradient of foreground is calculated. After pretreatment of edge detection and morphological operation, the depth information and infrared information are fused. The characteristics of depth map and infrared image in edge detection are used for the raised method, the false rate of detection is reduced, and detection precision is improved. Since the depth map and infrared image are not affected by natural sunlight, the influence on obstacle recognition due to the factors such as light intensity and shadow is effectively reduced and the robustness of the algorithm is also improved. Experiments indicate that the detection algorithm of information fusion can accurately identify the small obstacle in the view and the accuracy of obstacle recognition will not be affected by light. Hence, this method has great significance for mobile robot or intelligent vehicles on obstacle detection in outdoor environment.

The Implementation of Infrared Image Edge Detection Algorithm Based on CNN on FPGA

2012 ◽  
Vol 41 (11) ◽  
pp. 1354-1358 ◽  
Author(s):  
王巍 WANG Wei ◽  
安友伟 AN You-wei ◽  
黄展 HUANG Zhan ◽  
丁锋 DING Feng ◽  
杨铿 YANG Keng ◽  
...  
Keyword(s):  
Edge Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Image Edge

ROAD RECOGNITION TECHNOLOGY OF AGRICULTURAL NAVIGATION ROBOT BASED ON ROAD EDGE MOVEMENT OBSTACLE DETECTION ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 281-292 ◽  
Author(s):  
Na Yu ◽  
Qing Wang ◽  
Shichao Cao
Keyword(s):  
Motion Capture ◽  
Target Location ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Working Environment ◽  
Outdoor Environment ◽  
Analysis Model ◽  
Agricultural Robots ◽  
The Road ◽  
The Mean

In order to recognize the road effectively, agricultural robots mainly rely on the tracking and detection data of road obstacles. Traditional obstacle detection mainly studies how to use multiple fusion methods such as vision and laser to analyse structured and simplified indoor scenes. The working environment of agricultural robots is a typical unstructured outdoor environment. Therefore, based on the environmental characteristics of agricultural robot navigation, the mean displacement algorithm is introduced to detect and study the obstacles aiming at the road edge. After explaining the advantages and principle flow of the mean displacement algorithm to effectively realize motion capture, the feasibility of target location and tracking research is discussed. After that, the bottom data acquisition and analysis model is constructed based on the road navigation data of agricultural robots. To capture the movement obstacles of road edge and build the foundation of road recognition technology. In order to improve the effectiveness of motion obstacle capture and detection, a moving target detection algorithm is proposed to optimize and update the mean displacement algorithm, and constructs a feature-oriented hybrid algorithm motion capture model. The simulation results indicate that the proposed optimization model can effectively improve the tracking efficiency of non-rigid targets in outdoor environment, and the number of evaluation iterations can reach 3.5621 times per frame, which shows that the research has good theoretical and practical value.

A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images

2014 ◽  
Vol 1037 ◽  
pp. 411-415
Author(s):  
Dong Xing Li ◽  
Liang Geng ◽  
Qin Jun Du ◽  
Han Ren ◽  
Ai Jun Li ◽  
...  
Keyword(s):  
Edge Detection ◽  
Infrared Image ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Conversion Function ◽  
Gray Level ◽  
Infrared Images ◽  
Computation Complexity ◽  
Edge Information ◽  
Edge Detection Algorithm

The fuzzy edge detection algorithm proposed by Pal-King has some disadvantages for extracting the low gray level edge information for the infrared images, such as high computation complexity, low threshold segmentation inaccuracy and the leakage edge information. For overcoming the disadvantages, the improved image fuzzy edge detection algorithm is proposed in this paper. First, redefining membership function to simplify computation complexity, the new conversion function enable the function transform interval is [0, 1], thus the value of the low gray level edge is not to be set to 0. Second, Ostu's algorithm is used in the selection of segmentation threshold named as transit point. The traditional threshold value is improved in order to make the segmentation accurate. The experimental results show that the lower gray infrared image edge information is preserved via proposed algorithm in this paper. The detecting results are more accurate. The run time is decreased obviously than the traditional Pal - king algorithm.

An improved edge detection algorithm for depth map inpainting

2014 ◽  
Vol 55 ◽  
pp. 69-77 ◽  
Author(s):  
Weihai Chen ◽  
Haosong Yue ◽  
Jianhua Wang ◽  
Xingming Wu
Keyword(s):  
Edge Detection ◽  
Depth Map ◽  
Detection Algorithm ◽  
Edge Detection Algorithm

scholarly journals Novel MOA Fault Detection Technology Based on Small Sample Infrared Image

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1748
Author(s):  
Baoquan Wei ◽  
Yong Zuo ◽  
Yande Liu ◽  
Wei Luo ◽  
Kaiyun Wen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Recognition Rate ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Networks ◽  
Extended Model ◽  
Detection Technology

This paper proposes a novel metal oxide arrester (MOA) fault detection technology based on a small sample infrared image. The research is carried out from the detection process and data enhancement. A lightweight MOA identification and location algorithm is designed at the edge, which can not only reduce the amount of data uploaded, but also reduce the search space of cloud algorithm. In order to improve the accuracy and generalization ability of the defect detection model under the condition of small samples, a multi-model fusion detection algorithm is proposed. Different features of the image are extracted by multiple convolutional neural networks, and then multiple classifiers are trained. Finally, the weighted voting strategy is used for fault diagnosis. In addition, the extended model of fault samples is constructed by transfer learning and deep convolutional generative adversarial networks (DCGAN) to solve the problem of unbalanced training data sets. The experimental results show that the proposed method can realize the accurate location of arrester under the condition of small samples, and after the data expansion, the recognition rate of arrester anomalies can be improved from 83% to 85%, showing high effectiveness and reliability.

scholarly journals UAV low-altitude obstacle detection based on the fusion of LiDAR and camera

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhaowei Ma ◽  
Wenchen Yao ◽  
Yifeng Niu ◽  
Bosen Lin ◽  
Tianqing Liu
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Angle Measurement ◽  
Obstacle Detection ◽  
Accurate Estimation ◽  
Measurement Information ◽  
Depth Information ◽  
Performance Indexes ◽  
Low Altitude ◽  
Sensor Configuration

AbstractIn this paper, aiming at the flying scene of the small unmanned aerial vehicle (UAV) in the low-altitude suburban environment, we choose the sensor configuration scheme of LiDAR and visible light camera, and design the static and dynamic obstacle detection algorithms based on sensor fusion. For static obstacles such as power lines and buildings in the low-altitude environment, the way that image-assisted verification of point clouds is used to fuse the contour information of the images and the depth information of the point clouds to obtain the location and size of static obstacles. For unknown dynamic obstacles such as rotary-wing UAVs, the IMM-UKF algorithm is designed to fuse the distance measurement information of point clouds and the high precision angle measurement information of image to achieve accurate estimation of the location and velocity of the dynamic obstacles. We build an experimental platform to verify the effectiveness of the obstacle detection algorithm in actual scenes and evaluate the relevant performance indexes.

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