Abstract
Smart farming has always been one of the current research hotspots. Reflected by the behaviors and moves, the physiological conditions of pigs can be detected. The inability to detect the behaviors of pigs at scale has become the urgent issues. This makes the recognition of pigs an extremely significant problem. Building on the prior work on picture-based recognition of target detection, this paper put forward an improved YOLO V3 to detect pigs from image. To overcome the lack of pig’s pictures training data, transfer learning is used. To improve the accuracy of algorithm, attention mechanism is introduced into YOLO V3. Results show the algorithm we exploited can efficiently complete the task for pig real-time detection. Compared with the classical YOLO V3, the improved YOLO V3 has better metrics on precision, recall, F1 score and average precision. The improved model achieves result: 94.12% AP. The result is encouraging enough to make people collect more labeled pig’s picture data to improve the generalization capability of the algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hu, Z.P., Zhou, S.: Research on curvature - HOG target detection algorithm. J. Sig. Process, 36–41 (2013)
Yu, X.G.: Improved DPM and its application in pedestrian detection. National University of Defense Technology (2013)
J. H, G. Z.: A review of target detection algorithms for deep convolutional neural networks. Comput. Eng. Appl. 56(17), 12–23 (2020)
Girshick R.: Fast R-CNN. Computer Science (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2020). https://doi.org/10.1109/TPAMI.2018.2844175
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Computer Vision & Pattern Recognition, pp. 799–788. IEEE (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 6517–6525. IEEE (2017)
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
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv e-prints (2018)
Qing, Y.: Computer-generated image recognition method based on CNN. J. Southwest China Normal Univ. (Nat. Sci. Edn.) 44(05), 109–114 (2019)
Wang, H.L., Qi, X.L., Wu, G.S.: Research progress of target detection technology based on deep convolutional neural network. Comput. Sci. 45(09), 11–19 (2018)
Lu, H.T., Zhang, Q.C.: A review of the application of deep convolutional neural networks in computer vision. Data Acquisition Process. 31(1), 1–17 (2016)
Wen, H.B., Zhang, G.H.: Target detection method of driving video based on YOLO. Automob. Sci. Technol. 01, 73–76 (2019)
Hu, Y.Y., Wu, X.J., Zheng, G.D.: Object detection of UAV for anti-UAV based on improved YOLO v3. In: The 38th China Control Conference, pp. 885–889 (2019)
He, D.J., Liu, J.M., Xiong, H.T., et al.: An individual identification method for milking cows based on improved YOLO V3 model. J. Agric. Mach. 51(04), 250–560 (2020)
Liu, S.Z., Li, C.R., Liu, T.J., et al.: Cow target detection based on YOLO V3 model. J. Tarim Univ. 31(02), 85–90 (2019)
Chen, Q., Liu, L., Han, R., et al: Image identification method on high speed railway contact network based on YOLO v3 and SENet. In: The 38th China Control Conference (2019)
Li, S.Y., Liu, Y.H., Zhang, R.F.: A method for dog identification based on transfer learning and model fusion. Intell. Comput. Appl. 9(06), 101–106 (2019)
Wang, Z.W., Pu, Y.Y., Wang, X.: Multi-scale garment image precision retrieval based on multi-feature fusion. Chin. J. Comput. 4(43), 740–752 (2020)
Li, X.C., Liu, X.M., Chen, X.N.: Multi-target tracking algorithm based on YOLO detection. Comput. Eng. Sci. 42(4), 665–672 (2020)
Li, J., Gu, J., Huang, Z., et al.: Application research of improved YOLO V3 algorithm in PCB electronic component detection. Appl. Sci. 9(18), 3750 (2019)
Wei, H.B., Zhang, R.J., Du, G.M., et al.: Vegetable recognition algorithm based on improved YOLO V3. J. Zhengzhou Univ. (Eng. Sci. Edn.) 41(2), 7–12+31 (2020)
Jiao, T.C., Li, Q., Lin, M.S., et al.: Combination of anti-residual block and YOLOv3 target detection method. Transducer Microsyst. Technol. 38(9), 144–146+156 (2019)
Shi, H.: Research on vehicle Detection Algorithm based on deep convolutional neural network. Hunan Normal University (2019)
Ju, M.R., Luo, H.B., Wang, Z.B., et al.: Improved YOLO V3 algorithm and its application in small target detection. J. Opt. 39(7), 245–525 (2019)
Liu, D., Wu, Y.J., Luo, N.C., et al.: Gaussian-yolov3 target detection with embedded attention and feature interwoven modules. Comput. Appl. 40(8), 2225–2230 (2020)
Xu, C.J., Wang, X.F., Yang, Y.D.: Attention-yolo: YOLO detection algorithm with Attention mechanism. Comput. Eng. Appl. 55(6), 13–23+125 (2019)
Yan, J.T.: Research progress of computer vision technology in intelligent pig raising. Chin. J. Anim. Sci. 55(12), 38–42 (2019)
Li, H.Y.: Pedestrian recognition method based on Faster R-CNN, pp. 23–26. China Society of Automotive Engineers (2020)
Xue, D.X.: Research on image recognition model of white moth based on Faster R-CNN. J. Environ. Entomol. 42(06), 1502–1509 (2020)
Bai, J.: Student classroom behavior detection based on Faster R-CNN and transfer learning based on multi-channel feature fusion. J. Guangxi Normal Univ. (Nat. Sci. Edn.) 38(05), 1–11 (2020)
Huang, Y.F.: SE-Mask-RCNN: multi-parameter MRI prostate cancer segmentation method. J. Zhejiang Univ. (Eng. Sci.) (2021)
Shao, K.: A coronary artery segmentation method based on Mask RCNN fusion geometric features. J. Taiyuan Univ. Technol. (2020)
Acknowledgements
This work is supported by some people and company. I would like to thank my teachers, Mr. Tang and Mr. Yang, for their guidance and help in my thesis writing. Finally, thanks for the help of the company—“Hunan Baodong agriculture and animal husbandry company”.
Funding
This work was supported in part by the Hunan Province’s Strategic and Emerging Industrial Projects under Grant 2018GK4035, in part by the Hunan Province’s Changsha Zhuzhou Xiangtan National Independent Innovation Demonstration Zone projects under Grant 2017XK2058.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflicts of interest to report regarding the present study.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yin, D., Tang, W., Chen, P., Yang, B. (2021). Pig Target Detection from Image Based on Improved YOLO V3. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-78615-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78614-4
Online ISBN: 978-3-030-78615-1
eBook Packages: Computer ScienceComputer Science (R0)