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http://dx.doi.org/10.7472/jksii.2019.20.5.105

A Study on Model for Drivable Area Segmentation based on Deep Learning  

Jeon, Hyo-jin (Department of Computer Science and Information Engineering, Korea National University of Transportation)
Cho, Soo-sun (Department of Computer Science and Information Engineering, Korea National University of Transportation)
Publication Information
Journal of Internet Computing and Services / v.20, no.5, 2019 , pp. 105-111 More about this Journal
Abstract
Core technologies that lead the Fourth Industrial Revolution era, such as artificial intelligence, big data, and autonomous driving, are implemented and serviced through the rapid development of computing power and hyper-connected networks based on the Internet of Things. In this paper, we implement two different models for drivable area segmentation in various environment, and propose a better model by comparing the results. The models for drivable area segmentation are using DeepLab V3+ and Mask R-CNN, which have great performances in the field of image segmentation and are used in many studies in autonomous driving technology. For driving information in various environment, we use BDD dataset which provides driving videos and images in various weather conditions and day&night time. The result of two different models shows that Mask R-CNN has higher performance with 68.33% IoU than DeepLab V3+ with 48.97% IoU. In addition, the result of visual inspection of drivable area segmentation on driving image, the accuracy of Mask R-CNN is 83% and DeepLab V3+ is 69%. It indicates Mask R-CNN is more efficient than DeepLab V3+ in drivable area segmentation.
Keywords
Drivable area segmentation; Segmentation; Deep Learning; DeepLab V3+; Mask R-CNN; BDD Dataset;
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