Performance evaluation of color based road detection using neural nets and support vector machines

Conrad, Peter; Foedisch, M.

doi:10.1109/aipr.2003.1284265

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…Some methods suitable for building such a model are Neural Networks (NN), Support Vector Machines (SVM) and Partial Least Squares (PLS). These methods have successfully being used by researchers to determine road conditions based on camera images, or camera images together with meteorological data [10,[13][14][15]. As this sensor was a laboratory prototype used in a laboratory environment, it is not appropriate to develop a model before field tests are done.…”

Section: Cooling Liquid Inletmentioning

confidence: 99%

Remote sensor for winter road surface status detection

Jönsson

2011

2011 IEEE SENSORS Proceedings

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This paper suggests a cost effective remote sensor for increasing traffic safety by detecting road surface conditions. One limitation of existing systems is the ability to reliably detect the presence of ice and snow on the road surface. By utilizing infrared detectors sensitive in the water absorption spectral range, it is possible to remotely detect the presence of water on a surface. Using the near infrared spectra to detect water is well known, but further research is desired on methods to distinguish water in the form of water, ice and snow. Remote sensors are easy to install and they have low service costs compared to road mounted sensors. Existing remote sensors are currently expensive, but by utilizing cost effective infrared detectors a sensor has been made that can be deployed at any road weather information system. Laboratory results showed that the sensor gave reliable output that distinguishes between the surface conditions dry, wet, snowy and icy.

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Section: Cooling Liquid Inletmentioning

confidence: 99%

Remote sensor for winter road surface status detection

Jönsson

2011

2011 IEEE SENSORS Proceedings

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“…Previous research in our lab has focused on developing an adaptive method for parameter shifting that has been implemented through feature based classification using machine learning algorithms [13]- [14]. It is also to be noted that various machine learning algorithms are being widely utilized for the demarcation of off-road and on-road regions [15]- [16].…”

Section: Introductionmentioning

confidence: 99%

Generic vision based algorithm for driving space detection in diverse indoor and outdoor environments

Rasheed¹,

Ahmed²,

Ali³

et al. 2010

2010 IEEE International Conference on Mechatronics and Automation

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The detection of driving space is the most fundamental step in intelligent vehicle control. This research paper proposes a generic vision based algorithm for identifying driving surfaces in various indoor and outdoor environments. In this paper, instead of relying on a static model for demarcating the boundaries of the driving surfaces, we propose a novel algorithm that provides an adaptive method to detect a drivable surface in any environment. The uniqueness of the proposed algorithm lies in the robustness of the adaptive model that caters for changes in the environment. These changes may be in the form of light composition, off road disturbances, on road static and dynamic objects, shadows and variations in texture for indoor environment. It basically provides a highly dynamic online mechanism for changing the parameters of the Canny Edge Enhancement algorithm. This enables us to accurately determine the starting point and orientation of the driving surface boundary. Subsequently weighted average is used on the candidate edges to optimize the edge detection results. Experiments were carried out on our university's Intelligent DRIving System (IDRIS) for outdoor environments and on P3AT for indoor purposes. The experimentation results show that the proposed method can detect the driving surface boundaries in real-time for various different environments.

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“…K-means algorithm and densitybased spatial clustering of applications with noise (DBSCAN) algorithms are also significant means of clustering road regions [24,25]. Road detection is also implemented using a neural network [26]. Although it can achieve high accuracy, its performance often depends on the large amount of training data and complex computations.…”

Section: Introductionmentioning

confidence: 99%

Unpaved road detection based on spatial fuzzy clustering algorithm

Bao

Zhang

et al. 2018

J Image Video Proc.

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Vision-based unpaved road detection is a challenging task due to the complex nature scene. In this paper, a novel algorithm is proposed to improve the accuracy and robustness of unpaved road detection and boundary extraction with low computational costs. The novelties of this paper are as follows: (1) We use a normal distribution with infrared images to detect the vanishing line, and a trapezoid prediction model is proposed according to the road shape features. (2) Road recognition based on connected regions is implemented by an improved support vector machine (SVM) classifier with a normalized class feature vector. According to the recognition results, the road probability confidence map is obtained. (3) With the help of fusing continuous information with the trapezoidal forecasting model and the probability from the confidence map, we present a road probability recognition method based on the trapezoidal forecasting model and spatial fuzzy clustering. Furthermore, the histogram backprojection model is used to solve interference problems caused by shadows on the road. It takes approximately 0.012~0.014 s to process one frame of an image for the road recognition, and the accuracy rate can reach 93.2%. The experimental results show that the algorithm can achieve better performance than some state-of-the-art methods in terms of detection accuracy and speed.

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Performance evaluation of color based road detection using neural nets and support vector machines

Cited by 21 publications

References 4 publications

Remote sensor for winter road surface status detection

Remote sensor for winter road surface status detection

Generic vision based algorithm for driving space detection in diverse indoor and outdoor environments

Unpaved road detection based on spatial fuzzy clustering algorithm

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