Deep learning-based image recognition for autonomous driving

IATSS Research - Tập 43 - Trang 244-252 - 2019
Hironobu Fujiyoshi1, Tsubasa Hirakawa1, Takayoshi Yamashita1
1Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501, Japan

Tài liệu tham khảo

Cheng, 2016, Person re-identification by multi- channel parts-based cnn with improved triplet loss function, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Viola, 2001, Rapid object detection using a boosted cascade of simple features, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Dalal, 2005, Histograms of oriented gradients for human detection, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Csurka, 2004, Visual categorization with bags of keypoints Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 60, 91, 10.1023/B:VISI.0000029664.99615.94 Lecun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Ren, 2017, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137 Redmon, 2016, You only look once: unified, real-time object detection, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Long, 2015, Fully convolutional networks for semantic segmentation, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Zhao, 2017, Pyramid scene parsing network, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Cordts, 2016, The cityscapes dataset for semantic urban scene understanding, Proc. of IEEE Conference on Computer Vision and Pattern Recognition Moujahid, 2018, Machine learning techniques in ADAS: a review, Proc. of 2018 International Conference on Advances in Computing and Communication Engineering Levinson, 2011, Towards fully autonomous driving: systems and algorithms, Intelligent Vehicles Symposium, 163 Bojarski, 2016 Luona, 2018, Real-to-virtual domain unification for end-to-end autonomous driving, Proc. of European Conference on Computer Vision, 553 Mori, 2019, Visual explanation by attention branch network for end-to-end learning-based self-driving, Proc. of IEEE Intelligent Vehicles Symposium Li, 2013, A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios, IEEE Transactions on Vehicular Technology, 63, 540, 10.1109/TVT.2013.2281199 Lee, 2018, Development of a self-driving car that can handle the adverse weather, Int. J. Automot. Technol., 19, 191, 10.1007/s12239-018-0018-z Xu, 2017, End-to-end learning of driving models from large-scale video datasets, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Zhou, 2016, Learning deep features for discriminative localization, 2921, 10.1007/978-3-662-49373-1 Selvaraju, 2017, Grad-CAM: visual explanations from deep networks via gradient-based localization, 618 Bojarski, 2016 Kim, 2017, Interpretable learning for self-driving cars by visualizing causal attention, 2942 Kim, 2018, Textual explanations for self-driving vehicles, 563 Mori, 2019, Attention neural baby talk: captioning of risk factors while driving