Abstract
Shallow information is crucial in small object detection. Based on YOLOv5, an efficient small object detection algorithm (ES-YOLO) is proposed to improve identification accuracy using novel shallow feature extraction strategies. First of all, a detection head corresponding to shallow features is used to replace the original detection head corresponding to the deepest features in YOLOv5. Secondly, an attention module is directly added to the output layers of the backbone to filter redundant information and select representative original shallow features. Next, half of the inputs to the SPPF module are processed by the cross-stage partial connection method to reduce model parameters. Finally, the SIoU (SCYLLA-IoU) loss is used during the training stage to ensure fast convergence. Ablation studies are performed on two publicly available small object datasets. Results show that all the proposed models increase the model detection accuracy. Compared with the YOLOv5, the proposed model increases the identification accuracy by 2.4% and 3.5% on the BDD100K and VisDrone datasets, respectively. In addition, compared with the other 8 commonly used or up-to-date one-stage models, the proposed model achieves the best performance in identification accuracy. Source code is released in https://gitee.com/bai-hexiang/es-yolo.
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References
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020). https://doi.org/10.48550/arXiv.2004.10934
Cao, Y., Li, C., Peng, Y., Ru, H.: MCS-YOLO: a multiscale object detection method for autonomous driving road environment recognition. IEEE Access (2023). https://doi.org/10.1109/ACCESS.2023.3252021
Caputo, S., Castellano, G., Greco, F., Mencar, C., Petti, N., Vessio, G.: Human detection in drone images using YOLO for search-and-rescue operations. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds.) AIxIA 2021-Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, 1–3 December 2021, Revised Selected Papers, vol. 13196, pp. 326–337. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08421-8_22
Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016). https://doi.org/10.48550/arXiv.1603.07285
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021. arXiv preprint arXiv:2107.08430 (2021). https://doi.org/10.48550/arXiv.2107.08430
Gevorgyan, Z.: SIoU loss: more powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740 (2022). https://doi.org/10.48550/arXiv.2205.12740
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169
Girshick, R., Donahue, J., Darrell, T., Malik, J.: 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). https://doi.org/10.1109/CVPR.2014.81
Ji, S.J., Ling, Q.H., Han, F.: An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information. Comput. Electr. Eng. 105, 108490 (2023). https://doi.org/10.1016/j.compeleceng.2022.108490
Jocher, G.: YOLOv5. In: GitHub https://github.com/ultralytics/yolov5
Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424 (2022). https://doi.org/10.48550/arXiv.2206.02424
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). https://doi.org/10.1109/CVPR.2017.106
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018). https://doi.org/10.1109/CVPR.2018.00913
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Liu, Y., Sun, P., Wergeles, N., Shang, Y.: A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Appl. 172, 114602 (2021). https://doi.org/10.1016/j.eswa.2021.114602
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). https://doi.org/10.1109/CVPR.2016.91
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017). https://doi.org/10.1109/CVPR.2017.690
Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018). https://doi.org/10.48550/arXiv.1804.02767
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). https://doi.org/10.1109/TPAMI.2016.2577031
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vision 104, 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOV7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022). https://doi.org/10.48550/arXiv.2207.02696
Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020). https://doi.org/10.1109/CVPRW50498.2020.00203
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
Yang, R., Li, W., Shang, X., Zhu, D., Man, X.: KPE-YOLOv5: an improved small target detection algorithm based on YOLOv5. Electronics 12(4), 817 (2023). https://doi.org/10.3390/electronics12040817
Yu, F., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2636–2645 (2020). https://doi.org/10.1109/CVPR42600.2020.00271
Zhou, M., Li, J., Liu, S.: Fire detection based on improved-YOLOV5s. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds.) Artificial Neural Networks and Machine Learning-ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, 6–9 September 2022, Proceedings; Part IV, pp. 88–100. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15937-4_8
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., Ling, H.: Detection and tracking meet drones challenge. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7380–7399 (2021). https://doi.org/10.1109/TPAMI.2021.3119563
Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 2778–2788 (2021). https://doi.org/10.1109/ICCVW54120.2021.00312
Acknowledgement
The work is supported by the National Natural Science Foundation of China (No. 41871286) and the 1331 Engineering Project of Shanxi Province, China.
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Yang, M., Bai, H. (2023). A Shallow Information Enhanced Efficient Small Object Detector Based on YOLOv5. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14260. Springer, Cham. https://doi.org/10.1007/978-3-031-44195-0_1
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