Abstract
Although remarkable results have been achieved in the areas of object detection, the detection of small objects is still a challenging task now. The low resolution and noisy representation make small objects difficult to detect, and further recognition will be much harder. Aiming at the small objects that have regular positions, shapes, colors or other features, this paper proposes an approach of Regular and Small Target Detection based on Faster R-CNN (RSTD) for the detection and recognition of regular and small targets such as traffic signs. In this approach, a regular and small target feature extraction layer is designed to automatically extract the surrounding background and internal key information of the proposal objects, which benefits the detection and recognition. Extensive evaluations on Tsinghua-Tencent 100K and GTSDB datasets demonstrate the superiority of our approach in detecting traffic signs over well-established state-of-the-arts. The source code and model introduced in this paper are publicly available at: https://github.com/zhezheey/RSTD/.
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References
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2874–2883 (2016)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6154–6162 (2018)
Chen, X., et al.: 3D object proposals for accurate object class detection. In: Annual Conference on Neural Information Processing Systems (NIPS), pp. 424–432 (2015)
Cheng, P., Liu, W., Zhang, Y., Ma, H.: LOCO: local context based faster R-CNN for small traffic sign detection. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10704, pp. 329–341. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73603-7_27
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Annual Conference on Neural Information Processing Systems (NIPS), pp. 379–387 (2016)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Fang, W., Chen, J., Liang, C., Wang, X., Nan, Y., Hu, R.: Object detection in low-resolution image via sparse representation. In: International Conference on Multimedia Modeling (MMM), pp. 234–245 (2015)
Girshick, R.: Fast R-CNN. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1440–1448 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587 (2014)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)
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_23
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M. Igel, C.: Detection of traffic signs in real-world images: The German traffic sign detection benchmark. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2013)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia (MM), pp. 675–678 (2014)
Joly, A., Buisson, O.: Logo retrieval with a contrario visual query expansion. In: ACM International Conference on Multimedia (MM), pp. 581–584 (2009)
Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., Chen, Y.: RON: reverse connection with objectness prior networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5244–5252 (2017)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5325–5334 (2015)
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1951–1959 (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
Meng, Z., Fan, X., Chen, X., Chen, M., Tong Y.: Detecting small signs from large images. In: International Conference on Information Reuse & Integration for Data Science (IRI), pp. 217–224 (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Annual Conference on Neural Information Processing Systems (NIPS), pp. 91–99 (2015)
Shrivastava, A., Gupta, A.: Contextual priming and feedback for faster R-CNN. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 330–348. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_20
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556 (2014)
Yang, F., Choi, W., Lin, Y.: Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2129–2137 (2016)
Zhou, P., Ni, B., Geng, C., Hu, J., Xu, Y.: Scale-transferrable object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 528–537 (2018)
Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-sign detection and classification in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2110–2118 (2016)
Acknowledgement
This work is partially supported by the National Key R&D Program of China (No. 2018YFC0831500), the National Social Science Foundation of China (No. 16ZDA055), the National Natural Science Foundation of China (No. 61772082), and the Special Found for Beijing Common Construction Project.
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Wang, W., Wu, B., Lv, J., Dai, P. (2019). Regular and Small Target Detection. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_37
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