Abstract
This paper presents an Ensemble R-FCN framework for object detection. Specifically, we mainly make three contributions to our detection framework: (1) we augment the training images for R-FCN when facing the limited training samples and small object. (2) We further introduce several enhancement schemes to improve the performance of the single R-FCN. (3) An ensemble R-FCN is proposed to make our detection system more robust by combining different feature extractors and multi-scale inference. Experimental results demonstrate the advantages of the proposed method. Especially, our method achieved the performance of AP score 0.829 which ranked No. 1 among over 360 teams in Ucar Self-driving deep learning Competition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lowe, D.: Distinctive image features from scale-invariant key-points. In: IJCV (2004)
Dalal, N., Triggs, N.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Hoiem, D., Chodpathumwan, Y., Dai, Q.: Diagnosing error in object detectors. In: ECCV 2012 (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
Van de Sande, K.E., Uijlings, J.R., Gevers, T., Smeulders, A.W.: Segmentation as selective search for object recognition. In: ICCV (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: ECCV (2014)
Girshick, R.: Fast R-CNN. In: ICCV (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. arXiv preprint arXiv:1605.06409 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Li, D., Li, J., Nie, B., Sun, S.: Deconvolution single shot multibox detector for supermarket commodity detection and classification. In: ICDIP (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, J., Qian, J., Zheng, Y. (2018). Ensemble R-FCN for Object Detection. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_66
Download citation
DOI: https://doi.org/10.1007/978-981-10-7605-3_66
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7604-6
Online ISBN: 978-981-10-7605-3
eBook Packages: EngineeringEngineering (R0)