road detection




Self-supervised monocular road detection in desert terrain.
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We present a method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race. Instead of relying on a static, pre-computed road appearance model, this method adjusts its model to changing

Improving city model determination by using road detection from lidar data
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A new road classification technique from LIght Detection And Ranging (LIDAR) data is presented that relies on region growing in order to classify areas as road . The new method corrects some of the problems encountered with previously documented LIDAR road

Artificial neural networks for the detection of road junctions in aerial images
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Road junctions are important objects for traffic related tasks, vehicle navigation systems, but also have a major role in topographic mapping. The paper describes an approach of automatic junction detection using raster and vector information: mean and standard

Survey: Vision based road detection techniques
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Computer Vision based autonomous robot navigation has caught the attention of many researchers because of its huge potential in various industrial and social applications. This paper focuses on various approaches used to detect road region which is fundamental

UNSCARF-a color vision system for the detection of unstructured roads.
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This work focuses on the detection of roads in color images for robot navigation systems. Current road detection systems perceive certain types of roads under a limited number of conditions In general, road detection systems are specialized for specific scenarios

Road detection from high and low resolution satellite images
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Road extraction from satellite imagery has become a heated research subjects in recent years. It is especially used in the city planning, cartography and to update previously detected roads in Geographic Information Systems (GIS) environment. In this study

Road Detection With EOSResUNet and Post Vectorizing Algorithm.
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Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major

Road detection from high satellite images using neural networks
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In this paper, we propose a road detection model approach based on neural networks from satellite images. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems

Robust detection of road junctions in VHR images using an improved ridge detector
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In this paper we present a novel technique for automatic detection of road junctions in VHR satellite images. The detection is based on an initially detected road network using a differential ridge detector. This is a standard technique for road detection . However, the

Rural road detection of color image in complicated environment
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To realize rural road detection in complication environment, a method based on improved FCM (Fuzzy C-Means) and Hough transformation has been proposed. Firstly, the road image is transformed from RGB space into HIS space. Second, the initial clustering center is

Automatic road detection of satellite images A survey
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Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semiautomatic road network extraction techniques have significantly increased the

Markov random fields (MRF)-based texture segmentation for road detection
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Traffic observation from airplane platforms using digital cameras is a fairly new application of Video Image Detection Systems (VIDS). These systems are also particularly interesting for observations at major events or in catastrophe situations. Applied systems are small or

Evaluating color representations for on-line road detection
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Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions

Road detection using similarity search
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This paper concerns vision-based navigation of autonomous robots. We propose a new approach for road detection based on similarity database searches. Images from the camera are divided into regular samples and for each sample the most visually similar images are

Radar cross-views for road detection in dense urban areas
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This article studies the improvement of road detection using 2 SAR images of the same area taken with 2 different orientations. In the first part, some remarks about the visual appearance of the network in both images are made. In the second part, two experiments for

Automatic road extraction from high resolution satellite images using neural networks, texture analysis, fuzzy clustering and genetic algorithms
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presented. The proposed methodology consists of two separate stages of road detection and road vectorization. Neural networks are applied on high resolution IKONOS and Quick-Bird images for road detection . This paper

Vision based Real time Vehicle Guidance on THMR V Part I: Unstructured Road Detection
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Road Detection is one of the key problems in vision based vehicle guidance. In this paper, we present a real time unstructured road detection method. After preprocessing the original image, a low cost region segmentation procedure based on mathematical morphology is

Survey of work on road extraction in aerial and satellite images
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Section 5 discusses methods for performance evaluation. Finally, Section 6 proposes a road detection system and Section 7 gives our conclusions. 2 Road Characteristics In this section, we present different types of input data and goals for road detection systems

Road detection method for land consolidation using mathematical morphology from high resolution image
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Land consolidation is a tool for increasing the area of the arable land and improving the effectiveness of land cultivation. With the development of high resolution image, the progress of land consolidation project can be monitored by acquiring information from the

Road network detection by mathematical morphology
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An approach to achieve automated road network detection from digital images is presented. The method is based on mathematical morphology analysis while most road extraction algorithms are based on linear analysis methods. As the image resolution increases, road Detection of roads in urban areas is of greater importance and is a persistent research focus in the remote sensing community. The spectral and geometrical varieties of road pixels; their spectral similarity to other features such as buildings, parking lots, and sidewalks; and the

Road Detection Using Classification Algorithms.
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In this study, we present a road detection method. Proposed method consists of two phases. In the first phase, a binary image is obtained by utilizing greyscale transformation and thresholding processes. In the second phase, K-Nearest Neighbours and Naive Bayes

A novel neural network based voting approach for road detection via image entropy and color filtering
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Background: The dramatic increase in the traffic needs effective management that leads to the creation of intelligent transportation systems (ITS) or smart roads. Method: In this paper, a novel and quickly executable neural networks based method is presented to decide the

Performance evaluation of road detection and following algorithms
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We describe a methodology for evaluating algorithms to provide quantitative information about how well road detection and road following algorithms perform. The approach relies on generating a set of standard data sets annotated with ground truth. We evaluate the

Eye gaze tracking and eyes off the road detection for traffic safety on raspberry pi
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Most of the automobile accidents are caused by distracted driving. Passively monitoring drivers eyes can help in detecting state of mind and alertness of driver and thus can reduce risk of accidents. Proposed system includes three main parts 1) Facial feature tracking 2)

Road boundaries detection based on modified occupancy grid map using millimeter-wave radar
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Road region detection is a hot spot research topic in autonomous driving field. It requires to give consideration to accuracy, efficiency as well as prime cost. In that, we choose millimeter- wave (MMW) Radar to fulfill road detection task, and put forward a novel method based on

Road Detection from High Resolution Satellite Imagery Using Texture Parameters in Neural Network
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In this paper, neural networks are applied on high resolution IKONOS images for road detection . It was tried to optimize neural networks functionality using a variety of texture parameters with different window sizes and gray level numbers. Both the source image and

Improving road network extraction in high-resolution sar images by data fusion
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Two examples on real data prove the improvement brought by this two adding and the accuracy of the road detection 5 and 6. 4 CONCLUSIONS This paper has presented a road detection method that includes in a multi-scale framework a data fusion procedure

Lane detection using catmull-rom spline
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An edge-based road detection algorithm is presented by , it can work nicely in well painted roads even under shadow condition, but for an unpainted road that must be detected by its boundaries, this detection algorithm will meet problems Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle (UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road , which is not

StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation.
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General obstacle detection is a key enabler for obstacle avoidance in mobile robotics and autonomous driving. In this paper we address the task of detecting the closest obstacle in each direction from a driving vehicle. As opposed to existing methods based on 3D sensing

Automatic detection of changes in road data bases using satellite imagery
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An automatic procedure is developed for detection of changes between an existing road database and a newly registered satellite image, rectified to an orthophoto. The approach uses the existing database to force the detection and delineation of the corresponding road

Road segmentation by watershed algorithms
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crossing the road , etc.). 1 Page 3. In the first step, these two tasks require both an automatic road detection . This detection must work for any kind of road ( highway, country road , and so on) Our main work deals with the development of road detection algorithms. These

A neural machine vision model for road detection in autonomous navigation
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This paper presents an original approach for visual identification of road direction in autonomous vehicle navigation using a neural network classifier called Concurrent Self- Organizing Maps (CSOM), previously introduced by first author. For comparison, we also

Road plane detection using differential homography estimated by pair feature matching of local regions
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This paper presents a novel algorithm for road plane detection from an on-board camera. The algorithm employs the temporal difference of homography matrix, which is termed differential homography, caused by camera motion. Differential homography is estimated

Improved shadow removal for unstructured road detection
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One of the greatest challenges for vision-based road detection is the presence of shadows and other vehicles. Its particularly challenging to detect unstructured road when it has both shadowed and non shadowed area since the presence of shadows can cause hindrance

Road and traffic sign detection and recognition
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This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different

Application of machine vision technology IN road detection
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Machine vision was first applied in industrial manufacturing field, and now it is also used in road detection , with the rapid development and continuous innovation of computer technology and digital image processing technology. This study provides a detailed

Differential snakes for change detection in road segments
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Page 1. Differential Snakes for Change Detection in Road Segments Peggy Agouris, Anthony Stefanidis, and Sotirios Gyftakis from digital aerial and satellite imagery be found in Abstract Suetens et al. (1992), Gruen et al December 2001 1391 Page 2. the image, and hypotheses for