Abstract
Object detection is one of the most important and challenging branches in computer vision. Although impressive progress have been achieved on large or medium scale objects, detecting small objects from images is still difficult due to the limited image size and feature information. To deal with the small object detection problem, we explore how the popular YOLOv5 object detector can be modified to improve its performance on detecting small objects. To achieve this, we integrate Coordinate Attention (CA) and Context Feature Enhancement Module (CFEM) in YOLOv5 network. Coordinate Attention is based on attention mechanism and it embeds positional information into channel attention, which enables deep neural network to augment the representations of the objects of interest. Context Feature Enhancement Module explores rich context information from multiple receptive fields and only contains several additional layers. Extensive experimental results on the VisDrone-Detection dataset demonstrate that our approach can improve the performance of the proposed method for small object detection.
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References
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Wu, Q., Shen, C., Wang, P., Dick, A., Van Den Hengel, A.: Image captioning and visual question answering based on attributes and external knowledge. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1367–1381 (2017)
Zhao, Z.Q., Gao, J., Glotin, H., Wu, X.: A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering. Appl. Math. Model. 34(12), 3884–3895 (2010)
Zhao, Z., Wu, X., Lu, C., Glotin, H., Gao, J.: Optimizing widths with pso for center selection of gaussian radial basis function networks. Sci. China Inf. Sci. 57(5), 1–17 (2014)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
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, vol. 28 (2015)
Dai, J., Li, Y., He, K., Sun, J.: R-fcn: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
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)
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)
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
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1222–1230 (2017)
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874–2883 (2016)
Shrivastava, A., Sukthankar, R., Malik, J., Gupta, A.: Beyond skip connections: top-down modulation for object detection. arXiv preprint arXiv:1612.06851 (2016)
Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: Dssd: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659
Chen, C., Liu, M.-Y., Tuzel, O., Xiao, J.: R-cnn for small object detection. In: Lai, S.-H., Lepetit, V., Nishino, Ko., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10115, pp. 214–230. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54193-8_14
Ultralytics. YOLOv5 (2020). https://github.com/ultralytics/yolov5
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE cConference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
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)
Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578 (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Cao, J., Chen, Q., Guo, J., Shi, R.: Attention-guided context feature pyramid network for object detection. arXiv preprint arXiv:2005.11475 (2020)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systemsm vol. 30 (2017)
Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11(12), 520–527 (2007)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13713–13722 (2021)
Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q.: Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437 (2018)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Acknowlegment
This work was supported in part by the National Natural Science Foundation of China under Grant 61976079, in part by Guangxi Key Research and Development Program under Grant 2021AB20147, and in part by Anhui Key Research and Development Pro-gram under Grant 202004a05020039.
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Zhang, TY., Li, J., Chai, J., Zhao, ZQ., Tian, WD. (2022). Improved YOLOv5 Network with Attention and Context for Small Object Detection. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_29
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