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Two-Stage Training Method of RetinaNet for Bird’s Nest Detection

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Book cover Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1160))

Abstract

Common nesting materials such as branches, straws, and wires fall on high-voltage power lines causing short-circuit faults. In recent years, neural network has developed rapidly in the field of objects detection. Through the shooting of the drone and the base station camera, the neural network is used to identify and locate the bird’s nest in the image, which has great use prospects in the intelligent detection of the transmission system. RetinaNet is currently a representative objects detection network, using the focal loss to adjust the imbalance between foreground and background. In this paper, we apply RetinaNet to the bird’s nest detection of transmission systems. Due to the complex environment of the transmission system, the detector obtained by the single-stage training recognize the line equipment or other objects as the nest easily. Combining the experimental results of single-stage training, we propose a two-stage training method driven by false detection samples, which improves the performance of the detector.

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References

  1. Bai, X., Yang, X., Latecki, L.J.: Detection and recognition of contour parts based on shape similarity. Pattern Recogn. 41(7), 2189–2199 (2008)

    Article  Google Scholar 

  2. Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874–2883 (2016)

    Google Scholar 

  3. Bourdev, L., Brandt, J.: Robust object detection via soft cascade. In: 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 236–243. IEEE (2005)

    Google Scholar 

  4. Sun, C., Lam, K.-M.: Multiple-kernel, multiple-instance similarity features for efficient visual object detection. IEEE Trans. Image Process. 22(8), 3050–3061 (2013)

    Article  MathSciNet  Google Scholar 

  5. Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)

    Google Scholar 

  6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  7. Dvornik, N., Mairal, J., Schmid, C.: Modeling visual context is key to augmenting object detection datasets. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 364–380 (2018)

    Google Scholar 

  8. Felzenszwalb, P.F., Girshick, R.B., Mcallester, D.A.: Visual object detection with deformable part models. Commun. ACM 56(9), 97–105 (2013)

    Article  Google Scholar 

  9. Forsyth, D.: Object detection with discriminatively trained part-based models. Computer 2, 6–7 (2014)

    Article  Google Scholar 

  10. Frazier, S.D.: Birds, substations, and transmission. In: 2001 IEEE Power Engineering Society Winter Meeting. Conference on Proceedings (Cat. No. 01CH37194), vol. 1, pp. 355–358. IEEE (2001)

    Google Scholar 

  11. Georgakis, G., Mousavian, A., Berg, A.C., Kosecka, J.: Synthesizing training data for object detection in indoor scenes. arXiv preprint arXiv:1702.07836 (2017)

  12. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimed. 20(4), 985–996 (2017)

    Google Scholar 

  15. Li, J., Wei, Y., Liang, X., Jian, D., Yan, S.: Attentive contexts for object detection. IEEE Trans. Multimed. 19(5), 944–954 (2017)

    Article  Google Scholar 

  16. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  17. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  18. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  19. 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_2

    Chapter  Google Scholar 

  20. Mahendran, A., Vedaldi, A.: Visualizing deep convolutional neural networks using natural pre-images. Int. J. Comput. Vis. 120(3), 233–255 (2016)

    Article  MathSciNet  Google Scholar 

  21. Manikandan, M., Paranthaman, M., Aadithiya, B.N.: Detection of calcification form mammogram image using canny edge detector. Indian J. Sci. Technol. 11(20), 1–5 (2018)

    Article  Google Scholar 

  22. Ouyang, W., et al.: DeepID-Net: deformable deep convolutional neural networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2015)

    Google Scholar 

  23. Qing, C., Dickinson, P., Lawson, S., Freeman, R.: Automatic nesting seabird detection based on boosted HOG-LBP descriptors. In: 2011 18th IEEE International Conference on Image Processing, pp. 3577–3580. IEEE (2011)

    Google Scholar 

  24. Ren, S., Girshick, R., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  25. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  26. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  27. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  28. Wu, X., Yuan, P., Peng, Q., Ngo, C.W., He, J.Y.: Detection of bird nests in overhead catenary system images for high-speed rail. Pattern Recogn. 51(C), 242–254 (2016)

    Article  Google Scholar 

  29. Yang, Y., Wu, F.: Real-time traffic sign detection via color probability model and integral channel features. In: Li, S., Liu, C., Wang, Y. (eds.) CCPR 2014. CCIS, vol. 484, pp. 545–554. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45643-9_58

    Chapter  Google Scholar 

  30. Yu, Y., Gong, Z., Zhong, P., Shan, J.: Unsupervised representation learning with deep convolutional neural network for remote sensing images. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 97–108. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_9

    Chapter  Google Scholar 

  31. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Ruidian Chen & Jingsong He

Authors
  1. Ruidian Chen
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  2. Jingsong He
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Correspondence to Ruidian Chen or Jingsong He .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Chen, R., He, J. (2020). Two-Stage Training Method of RetinaNet for Bird’s Nest Detection. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_49

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  • DOI: https://doi.org/10.1007/978-981-15-3415-7_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3414-0

  • Online ISBN: 978-981-15-3415-7

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