scholarly journals Self-supervised Monocular Road Detection in Desert Terrain

2006 ◽  
Author(s):  
H. Dahlkamp ◽  
A. Kaehler ◽  
D. Stavens ◽  
S. Thrun ◽  
G. Bradski
Keyword(s):  
Road Detection

CRF based road detection with multi-sensor fusion

2015 ◽  
Author(s):  
Liang Xiao ◽  
Bin Dai ◽  
Daxue Liu ◽  
Tingbo Hu ◽  
Tao Wu
Keyword(s):  
Sensor Fusion ◽  
Road Detection

Real-time unstructured road detection based on CNN and Gibbs energy function

2021 ◽  
pp. 1-19
Author(s):  
Mingzhou Liu ◽  
Xin Xu ◽  
Jing Hu ◽  
Qiannan Jiang
Keyword(s):  
Gibbs Energy ◽  
Energy Function ◽  
Gaussian Mixture Models ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Gibbs Energy Function ◽  
Road Detection ◽  
The Road ◽  
Detection Algorithms ◽  
Random Sample Consensus

Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels.

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