A Fast Orientation Invariant Detector Based on the One-stage Method

Open Access

Issue		MATEC Web Conf. Volume 232, 2018 2018 2^nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)


Article Number		04036
Number of page(s)		7
Section		Circuit Simulation, Electric Modules and Displacement Sensor
DOI		https://doi.org/10.1051/matecconf/201823204036
Published online		19 November 2018

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, A.C. Berg. Single Shot MultiBox Detector. European Conference on Computer Vision, 21-37 (2016) [Google Scholar]
J. Redmon, S. Divvala, R. Girshick, A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection. IEEE Conference on Computer Vision and Pattern Recognition, 779-788 (2016) [Google Scholar]
J. Redmon, A. Farhadi. YOLO9000: Better, Faster, Stronger. IEEE Conference on Computer Vision and Pattern Recognition, 6517-6525 (2016) [Google Scholar]
J. Redmon, A. Farhadi. YOLOv3: An Incremental Improvement. IEEE Conference on Computer Vision and Pattern Recognition (2018) [Google Scholar]
R. Girshick, J. Donahue, T. Darrell, J. Malik. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, 580-587 (2014) [Google Scholar]
R. Girshick. FastR_CNN. Computer science, 1440-1448(2015) [Google Scholar]
S. Ren, K. He, R. Girshick, J. sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39,6(2017) [Google Scholar]
K. He, G. Gkioxari, P. Dollar, R. Girshick. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP, 99(2017) [Google Scholar]
J. Dai, Y. li, K. He, J. Sun. Object Detection via Region-based Fully Convolutional Networks. Neural information processing systems, 379-387(2016) [Google Scholar]
K. He, X Zhang, S. Ren, J. Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37,9 (2015) [Google Scholar]
K. Sande, J. Uijlings, T. Gevers, A. Smeulders. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE International Journal of Computer Vision, 104,2(2013) [Google Scholar]
M. Everingham, LV. Gool, CKI. Williams, J. Winn, A. Zisserman. The Pascal Visual Object Classes (VOC) Challenge. International Journal of computer vision, 88,2(2010) [Google Scholar]
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona. Microsoft COCO: Common Objects in Context. European conference on computer vision, 740-755(2014) [Google Scholar]
G. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, et al. DOTA: A Large-scale Dataset for Object Detection in Aerial Images (2018) [Google Scholar]
P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1,2(2001) [Google Scholar]
P. Felzenszwalb, R. Girshick, D. Mcallester. Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32,9(2010) [Google Scholar]
A. Krizhevsky, I. Sutskever, G. Hinton, et al. ImageNet Classification with Deep Convolutional Neural Networks. Neural information processing systems, 1097-1105(2012) [Google Scholar]
K. Simonyan, A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. International conference on learning representations(2015) [Google Scholar]
M. Zeile, R. Fergus. Visualizing and Understanding Convolutional Networks. European conference on computer vision, 818-833(2014) [Google Scholar]
K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770-778(2016) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.