Abstract
Deep Convolutional Neural Networks (CNNs) have induced significant progress in the field of computer vision including object detection and classification. Two-stage detectors like Faster RCNN and its variants are found to be more accurate compared to its one-stage counter-parts. Faster RCNN combines an ImageNet pretrained backbone network (e.g VGG16) and a Region Proposal Network (RPN) for object detection. Although Faster RCNN performs well on medium and large scale objects, detecting smaller ones with high accuracy while maintaining stable performance on larger objects still remains a challenging task. In this work, we focus on designing a robust backbone network for Faster RCNN that is capable of detecting objects with large variations in scale. Considering the difficulties posed by small objects, our aim is to design a backbone network that allows signals extracted from small objects to be propagated right through to the deepest layers of the network. This being our motivation, we propose a robust network: BackNet, which can be integrated as a backbone into any two-stage detector. We evaluate the performance of BackNet-Faster RCNN on MS COCO dataset and show that the proposed method outperforms five contemporary methods.
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References
Teng, S.W., Hossain, M.T., Lu, G.: Multimodal image registration technique based on improved local feature descriptors. J. Electron. Imaging 24(1), 013013 (2015)
Fattal, A.-K., Karg, M., Scharfenberger, C., Adamy, J.: Distant vehicle detection: how well can region proposal networks cope with tiny objects at low resolution? In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 289–304. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_17
Gao, Y., et al.: Scale optimization for full-image-CNN vehicle detection. Paper presented at the 2017 IEEE Intelligent Vehicles Symposium (IV) (2017)
Girshick, R. Fast R-CNN. Paper presented at the proceedings of the IEEE international conference on computer vision (2015)
Yang, W., Li, S., Ouyang, W., Li, H., Wang, X. Learning feature pyramids for human pose estimation. Paper presented at the proceedings of the IEEE international conference on computer vision (2017)
Zhou, P., Ni, B., Geng, C., Hu, J., Xu, Y. Scale-transferrable object detection. Paper presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2018)
Yang, F., Choi, W., Lin, Y. Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. Paper presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2016)
Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Paper presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2016)
Hu, P., Ramanan, D.: Finding tiny faces. Paper presented at the proceedings of the IEEE conference on computer vision and pattern recognition (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
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)
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
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Detnet: a backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)
Chen, X., Gupta, A.: An implementation of faster RCNN with study for region sampling. arXiv preprint arXiv:1702.02138 (2017)
Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6354, pp. 92–101. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15825-4_10
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
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
Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 354–370. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_22
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: Discriminatively trained deformable part models, release 5 (2012)
Zhang, N., Farrell, R., Iandola, F., Darrell, T.: Deformable part descriptors for fine-grained recognition and attribute prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 729–736 (2013)
Azizpour, H., Laptev, I.: Object detection using strongly-supervised deformable part models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 836–849. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_60
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conf. CVPR, vol. 2, pp. 886–893, June 2005
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: 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)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2015)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Singh, B., Davis, L.S.: An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3578–3587 (2018)
Hossain, M.T., Teng, S.W., Zhang, D., Lim, S., Lu, G.: Distortion robust image classification using deep convolutional neural network with discrete cosine transform. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 659–663 (2019)
Wang, W., Wu, B., Lv, J., Dai, P.: Regular and Small Target Detection. Paper presented at the international conference on multimedia modeling (2019)
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This work has been Funded by Federation University Australia research grants.
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Hossain, M.T., Teng, S.W., Lu, G. (2019). BackNet: An Enhanced Backbone Network for Accurate Detection of Objects with Large Scale Variations. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_5
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