کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9952254 | 1444170 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combining CNN and MRF for road detection
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Road detection aims at detecting the road surface ahead of the vehicle and plays a crucial role in driver assistance systems. To improve the accuracy and robustness of road detection approaches in complex environments, a new road detection method based on a convolutional neural network (CNN) and Markov random field (MRF) is proposed. The original road image is segmented into super-pixels of uniform size using the simple linear iterative clustering (SLIC) algorithm. On this basis, we train the convolutional neural network, which can automatically learn the features that are most beneficial to the classification. The trained convolutional neural network (CNN) is then applied to classify road and non-road regions. Finally, based on the relationship between the super-pixel neighborhood, we utilize Markov random field (MRF) to optimize the classification results of the convolutional neural network (CNN). The approach provides the better performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 70, August 2018, Pages 895-903
Journal: Computers & Electrical Engineering - Volume 70, August 2018, Pages 895-903
نویسندگان
Geng Lei, Sun Jiangdong, Xiao Zhitao, Zhang Fang, Wu Jun,