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road detection and vehicle tracking by vision for adaptive cruise control

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2001年第4期20卷 267-286页

作者： Aufrère, R Marmoiton, F Chapuis, R Collange, F Dérutin, JP Univ Clermont Ferrand 2 Lab Sci & Mat Elect & Automat F-63177 Clermont Ferrand France

This article deals first with a process designed to detect the circulation lane of a vehicle by onboard monocular vision. This detection process is based on a recursive updating of a statistical model of the lane obtained by a training phase. Once the lane has been located. a reconstruction algorithm computes the vehicle location on its lane and the three-dimensional shape of the road. Thereafter, the authors seek to detect and track vehicles situated in front of their vehicle and equipped with specific visual markers in order to achieve an accurate determination of their location and speed. By combining these various data. the most dangerous obstacle can be identified Each of these three processes is described in detail, and significant examples are provided.

关键词： road detection three-dimensional reconstruction vehicle tracking

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road detection from airborne LiDAR point clouds adaptive for variability of intensity data

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OPTIK 2015年第23期126卷 4292-4298页

作者： Li, Yong Yong, Bin Wu, Huayi An, Ru Xu, Hanwei Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Jiangsu Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Jiangsu Peoples R China

This paper presents a novel algorithm of road detection from airborne LiDAR point clouds adaptive for variability of intensity data of road network. First, the point cloud topology is constructed using a grid index structure which facilitate spatial searching and preserves the accuracy of raw data without interpolation, and a LiDAR filtering algorithm is employed to distinguish the ground points from non-ground points. Second, road candidates are identified in the derived ground points by segmentation based on local intensity distribution histogram. Finally, the ultimate road point sets are verified by global inference based on the roughness and area of the road candidate point sets. The roughness of candidate point sets are calculated based on morphological gradients in consideration of the characteristics of roads compared to other non-road ground areas such as grass land and bare ground. The experimental results using practical data in complex environment demonstrate that this algorithm is able to automatically detect roads adaptive for the variability of intensity data of road network. Other non-road ground areas such as grass land and bare ground can be efficiently eliminated. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： road detection LiDAR Histogram Roughness Mathematical morphology

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road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes

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ROBOTICS AND AUTONOMOUS SYSTEMS 2016年 85卷 1-11页

作者： Li Yong Ding Weili Zhang XuGuang Ju Zhaojie Yanshan Univ Inst Elect Engn Dept Automat Lab Pattern Recognit & Intelligent SystKey Lab I Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Coll Elect Engn Qinhuangdao 066004 Hebei Peoples R China Univ Portsmouth Sch Comp Portsmouth P01 3HE Hants England

Vision-based road extraction is essentially important in many fields, such as for intelligent traffic and robot navigation. However, the road detection in urban or ill-structured roads is still very challenging at current stage, and the existing methods often suffer from high computational complexity. This paper reports a novel and efficient method for road detection in challenging scenes. First, the dark channel based image segmentation is proposed to distinguish a rough road region from complex background noise, which is envisioned to reduce the workload of road detection. Furthermore, instead of using the conventional pixel-wise soft voting, a new voting strategy based on the vanishing point and the properties of the segmented regions is proposed to further reduce the computation time of road extraction stage. Finally, the segmented region which has the maximum voting value is selected as the road region. Experimental results demonstrated that the proposed algorithm shows superior performance in different kinds of road scenes. It can remove the interference from pedestrians, vehicles and other obstacles. Our method is about 40 times faster in detection speed, when compared to a recently well-known approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Machine vision Dark channel Image segmentation road detection

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road detection Network Based on Anti-Disturbance and Variable-Scale Spatial Context Detector

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IEEE ACCESS 2021年 9卷 114640-114648页

作者： Ding, Qichen Liu, Hongkun Luo, Haokun Chen, Xueyun Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

road detection plays a critical role in the application of smart transportation. The performance of the mainstream methods such as PSPNet (Pyramid Scene Parsing Network), DeepLab V3, FCRN (Fully Convolutional Residual Networks) still suffers from uncertain disturbances of surface abrasion buildings, pedestrians, and variation of illumination like tree-shadow. The extracted features are vulnerable to extra-disturbance, and non-local spatial-context information has not been fully utilized. In this paper, a detector based on anti-disturbance and variable-scale spatial context features (AVD) is proposed: the training of the multi-layer features of the detector is always taken under the imposing of fake-feature-disturbance from an independent generator, which is trained to exacerbate the detector errors and the mistakes of feature discriminator. The detector is prepared to be immune from the fake-feature-disturbance, and the discriminator is trained to distinguish the differences between the non-interference features and disturbing features. We also designed a novel variable-scale spatial context module to enhance the richness performance of the extracted features. And a soft connection link is bridged between the low and high feature layers. The detection experiments on the Munich road dataset and urban road dataset show that AVD is better than all the mainstream above methods. Our method increases the accuracy by 3% on the Munich remote sensing dataset and 0.4% on the urban road dataset. Our code and datasets are available at https://***/Ding-Q/AVD for download.

关键词： Feature extraction roads Detectors Generators Decoding Semantics Generative adversarial networks Object detection convolutional neural network anti-disturbance generative adversarial network road detection remote sensing road detection

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road detection and Tracking from Aerial Desert Imagery

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2012年第1-4期65卷 345-359页

作者： Lin, Yucong Saripalli, Srikanth Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85287 USA

We present a fast, robust road detection and tracking algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 96% of the images. We experimentally validated our algorithm on over a thousand aerial images obtained using our UAV. These images consist of straight and curved roads in various conditions with significant changes in lighting and intensity. We have also developed a road-tracking algorithm that searches a local rectangular area in successive images. Initial results are presented that shows the efficacy and the robustness of this algorithm. Using this road tracking algorithm we are able to further improve the road detection and achieve a 98% accuracy.

关键词： road detection road tracking Unmanned Aerial Vehicles Hough transform

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road detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第9期57卷 7209-7220页

作者： Yang, Xiaofei Li, Xutao Ye, Yunming Lau, Raymond Y. K. Zhang, Xiaofeng Huang, Xiaohui Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Dept Comp Sci Shenzhen 518055 Peoples R China Shenzhen Key Lab Internet Informat Collaborat Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Informat Syst Hong Kong Peoples R China East China Jiaotong Univ Sch Informat Engn Comp Sci Nanchang 330013 Jiangxi Peoples R China

road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods to solve the problem. However, these approaches may suffer from the well-known problem of propagation errors. In this paper, we propose a novel deep learning model, recurrent convolution neural network U-Net (RCNN-UNet), to tackle the aforementioned problem. Our proposed RCNN-UNet has three distinct advantages. First, the end-to-end deep learning scheme eliminates the propagation errors. Second, a carefully designed RCNN unit is leveraged to build our deep learning architecture, which can better exploit the spatial context and the rich low-level visual features. Thereby, it alleviates the detection problems caused by noises, occlusions, and complex backgrounds of roads. Third, as the tasks of road detection and centerline extraction are strongly correlated, a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Extensive experiments were carried out based on two publicly available benchmark data sets, and nine state-of-the-art baselines were used in a comparative evaluation. Our experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks.

关键词： Recurrent convolutional neural network (RCNN) road centerline extraction road detection U-Net

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road detection from Optical Remote Sensing Imagery Using Circular Projection Matching and Tracking Strategy

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2013年第4期41卷 819-831页

作者： Fu, Gang Zhao, Hongrui Li, Cong Shi, Limei Tsinghua Univ Dept Civil Engn Inst Geomat Beijing 100084 Peoples R China

A road detection method based on Circular Projection (CP) matching and tracking strategy is proposed in this paper. The CP matching is a template matching-based algorithm with rotation invariant and low computation characteristics. This advantage is beneficial for twisty roads detection. Take the good use of the advantage, we improve the CP matching to make it more applicable for road detection, including: optimal template radius selection, approximation control parameter, and anti-interference correction for suppressing brightness and random noise affects. The overall road detection is using a dual-directional tracking strategy. In road tracking, we use a fan-shaped scope to limit the direction of template matching to prevent backtracking. In tracking procedure, we propose an adaptive template matching algorithm, overcoming changes between the selected template and targets. Finally, SPOT-5 and GeoEye-1 remote sensing images are used for experiment. The experimental result shows that the proposed method is rotation invariant, flexible and fast, which is applicable to the twisty road detection. The experiment results also show that road detection error is about 2 similar to 5 m, and detection accuracy is higher than 80 %.

关键词： Circular projection Remote sensing imagery road tracking road detection Twisty road

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road detection Method Corresponded to Multi road Types with Flood Fill and Vehicle Control

Road Detection Method Corresponded to Multi Road Types with ...

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2nd International Conference on Robot, Vision and Signal Processing (RVSP)

作者： Fukukawa, Tomoya Maeda, Yu Sekiyama, Kosuke Fukuda, Toshio Nagoya Univ Dept Mech Sci & Engn Chikusa Ku Nagoya Aichi 4648603 Japan Nagoya Univ Dept Micronano Syst Engn Chikusa Ku Nagoya Aichi 4648603 Japan Meijo Univ Fac Sci & Engn Tenpaku Ku Nagoya Aichi 4688502 Japan

ISBN: (纸本)9781479931842

This paper proposes the road detection method corresponded to multi road types with Flood Fill. Flood Fill is one of the image processing methods to partition the region of input image based on RGB color model. road detection is useful for automatic robots because the robots work on various road surface in outdoor environment. The proposed method has two features. Firstly, the method can cancel the influence of shadow on road by using HSV color model. Secondly, the method can recognize multi road types by k-nearest neighbor algorithm. By using the proposed method, the robot can select the suitable controller for road surface or the safety route. We implement the proposed method in vehicle navigation and the availability is verified by the experimental results.

关键词： Image Processing Flood Fill road detection

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road detection statistics for automated quality control of GIS data

Road detection statistics for automated quality control of G...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Goeman, W Gautama, S Philips, W D'Haeyer, J State Univ Ghent Dept Telecommun & Informat Proc B-9000 Ghent Belgium

ISBN: (纸本)0780387422

In this work, we examine the use of road detection in VHR satellite images to automate the process of quality assessment of digital road network data. An important aspect is the emphasis on accuracy and reliability of the system. Although road detection has been studied for more than a decade, it is often difficult to assess what performance can be expected over datasets other than the images published. We propose a system to train a road detector based on image examples of typical roads. The system calculates the optimal detection parameters and estimates the performance over the dataset in terms of detection rate and degree of fragmentation. The methodology relies on error propagation and image statistics, and is generic in nature. By showing image examples, variations due to shadow, activity on the road, weather conditions etc. can be taken into account when estimating the expected performance.

关键词： road detection error propagation segment modeling

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road detection IN A FOREST USING SENTINEL-1 AND FBR TIME-SERIES SPECKLE FILTERING

ROAD DETECTION IN A FOREST USING SENTINEL-1 AND FBR TIME-SER...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Michenot, Florent Hinostroza, Israel Guinvarc'h, Regis Thirion-Lefevre, Laetitia Univ Paris Saclay SONDRA CentraleSupelac F-91190 Gif Sur Yvette France

ISBN: (纸本)9798350320107

Temporal and spatial information is exploited in order to improve road detection performance in a forested area. Speckle is filtered using an FBR-based SAR time-series speckle filter, thus preserving temporal information. The additional spatial information is provided by the ascending and descending directions of acquisition of the satellite. Instead of a detection being made if the pixel value is lower than the threshold, we require the threshold to be met for every date in the time-series, and from both directions at once. This corresponds to both a temporal and spatial (generally East-West) stability requirements, and tends to reduce false positives as forest is not a stable environment. The technique was applied on a Sentinel-1 dataset over an area in northern Belize.

关键词： road detection SAR time-series Sentinel-1

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