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Object Detection Based on Multiscale Merged Feature Map

  • Zhaohui Luo
  • Hong Zhang
  • Zeyu Zhang
  • Yifan Yang
  • Jin Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

In object detection, high quality feature map is of great importance for both object location and classification. This paper presents a new network architecture to get higher quality feature map, which combines the feature map from shallow convolution layers with deep convolution layers by up–sampling and concatenating. It adopts a one-stage network, which does not rely on region proposal, to directly predict the location and classification of objects using the high quality feature map. With the input images of size 300 * 300, this network can be trained efficiently to achieve solid results on well-known object detection benchmarks: 77.7% on VOC2007, outperforming a comparable state of the art SSD [1], YOLO [5] and Faster R-CNN [4] model.

Keywords

Object detection Up-sampling Concatenating Feature map 

References

  1. 1.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  2. 2.
    Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  3. 3.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  4. 4.
    Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  5. 5.
    Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  6. 6.
    Uijlings, J.R.R., Van De Sande, K.E.A., Gevers, T., et al.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)CrossRefGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10578-9_23CrossRefGoogle Scholar
  8. 8.
    Sermanet, P., Eigen, D., Zhang, X., et al.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
  9. 9.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  10. 10.
    Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision 1998, pp. 555–562. IEEE (1998)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  13. 13.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  14. 14.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  15. 15.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  16. 16.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  18. 18.
    Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI, pp. 4278–4284 (2017)Google Scholar
  19. 19.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. IJCV 88, 303–338 (2010)CrossRefGoogle Scholar
  20. 20.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115, 211–252 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhaohui Luo
    • 1
  • Hong Zhang
    • 1
  • Zeyu Zhang
    • 1
  • Yifan Yang
    • 1
  • Jin Li
    • 1
  1. 1.Image Processing CenterBeihang UniversityBeijingChina

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