Research and Implementation of Driving Concern Area Detection Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.18-CVR0694

Abstract

Abstract: As a key technology of intelligent driving, driving concern area detection method has an important impact on the performance of intelligent driving or intelligent early warning system. In view of the shortcomings of the existing methods, this paper proposes an effective method for driving concern area detection based on the deep learning. We obtain the camera internal and external parameters by using camera self-calibration method based on camera model, use the Canny edge detection and Bisecting K-means clustering to realize the vanishing point estimation, and establish the road detection model based on the obtained estimates. We obtain the depth features from the SSD model training, use the convolution layer of SSD which combines with the upper sampling layer of FCN8 to detect the region of the road surface. The experimental results show that compared with the existing methods, the proposed method not only has better road detection effect, but also can detect the road area of the shaded part more accurately.

Key words: Intelligent driving, driving concern area, deep learning, road detection

CLC Number:

TP391

Ye Jihua, Shi Shuxia, Li Hanxi, Wang Shimin, Yang Siyu. Research and Implementation of Driving Concern Area Detection Based on Deep Learning[J]. Journal of System Simulation, 2019, 31(7): 1421-1428.

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