Abstract
With the wide application and rapid development of digital media technology, the interaction between people and computers has become an important part of people’s daily life. Pig detection using computer vision is an important technology for realizing fine pig management, real-time monitoring of pig growth and prediction of pig production. In the actual breeding environment, the accurate detection of pigs is difficult, and factors such as target occlusion and small targets seriously affect the accuracy of pig detection. We take a group of healthy pigs in a real breeding environment as the research object and propose a lightweight pig detection method based on YOLOv3-tiny. The method first uses Removal Net to replace YOLOv3-tiny’s backbone network, which improves the accuracy and speed of the detection method. Moreover, a new prediction branch is added to the prediction network to improve the detection accuracy for small objects. Then the soft non-maximum suppression(Soft-NMS) algorithm replaces the NMS algorithm in YOLOv3-tiny, which improves the detection ability for occluded objects. Finally, the feasibility and superiority of this method are proved by several groups of comparative tests. The experimental results indicate that our proposed pig-based detection method based on computer vision can provide an effective reference for refined management and real-time monitoring of pigs.
Similar content being viewed by others
References
Andrew W, Greatwood C, Burghardt T (2017) Visual localisation and individual identification of holstein friesian cattle via deep learning. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2850–2859
Bodla N, Singh B, Chellappa R, et al. (2017) “Soft-NMS-improving object detection with one line of code.” Proceedings of the IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 5561–5569
Chen X, Xu Y, Wong DW, Wong TY, Liu J (2015) “Glaucoma detection based on deep convolutional neural network.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) pp.715–718
Girshick R (2015) “Fast R-CNN.” Proceedings IEEE Int Conf Comput Vis, 1440-1448
Han S, Zhang J, Zhu M, Wu J, Kong F (2017) Review of automatic detection of pig behaviours by using image analysis. IOP Conf Ser Earth Environ Sci 69(1):012096
He K, Gkioxari G, Dollár P Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125
Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, pp 21–37
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Ma C, Li Y, Yin G, Ji J (2012) The monitoring and information management system of pig breeding process based on internet of things. In: 2012 Fifth International Conference on Information and Computing Science, pp 103–106
Neubeck A, Gool L J V (2006) “Efficient non-maximum suppression.” International Conference on Pattern Recognition. IEEE Computer Society
Omidyeganeh M, Shirmohammadi S, Abtahi S, Khurshid A, Farhan M, Scharcanski J, Hariri B, Laroche D, Martel L (2016) Yawning detection using embedded smart cameras. IEEE Trans Instrum Meas 65(3):570–582
Psota ET, Mittek M, Pérez LC, Schmidt T, Mote B (2019) Multi-pig part detection and association with a fully-convolutional network. Sensors 19(4):852
Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271
Redmon J, Farhadi A (2018) “YOLOv3: An incremental improvement.” [2022-03-03]. USA: https://arxiv.org/abs/1804.02767
Redmon J, Divvala S, Girshick R, et al. (2016) “You only look once: unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition pp 779-788
Ren S, He K, Girshick R, et al. (2015) “Faster R-CNN: towards real-time object detection with region proposal networks.” Adv Neural Inf Process Syst91-99
Sa I, Ge Z, Dayoub F et al (2016) DeepFruits: A fruit detection system using deep neural networks. Sensors 16(8):1222
Sa J, Choi Y, Lee H, Chung Y, Park D, Cho J (2019) Fast pig detection with a top-view camera under various illumination conditions. Symmetry. 11:266 (2019)
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netwo Off J Int Neural Netw Soc 61:85–117
Seo J, Ahn H, Kim D, Lee S, Chung Y, Park D (2020) EmbeddedPigDet—fast and accurate pig detection for embedded board implementations. Appl Sci 2020(10):2878
Shafiee MJ, Chywl B, Li F, Wong A (2017) Fast YOLO: A fast you only look once system for real-time embedded object detection in video, pp 1709–1712
Shi R, Li T, Yamaguchi Y (2020) An attribution-based pruning method for real-time mango detection with YOLO network. Comput Electron Agric 169:105214
Sun S, Qin J, Xue H (2019) Sheep delivery scene detection based on faster-RCNN. In: 2019 International Conference on Image and Video Processing, and Artificial Intelligence, pp 297–303
Wang J, Aozhi L, Jing X (2018) “Video-based pigs recognition with feature-integrated transfer learning.” Biom Recognition, pp.620–631
Xiao D, Shan F, Li Z, Le BT, Liu X, Li X (2019) A target detection model based on improved tiny-Yolov3 under the environment of mining truck. IEEE Access 7:123757–123764
Yang Z, Xu W, Wang Z, He X, Yang F, Yin Z (2019) Combining YOLOV3-tiny model with dropblock for tiny-face detection. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT), pp 1673–1677
Zhang L, Gray H, Ye X, Collins L, Allinson N (2019) Automatic individual pig detection and tracking in pig farms. Sensors 19(5):1188
Zhiqiang W, Jun L (2017) A review of object detection based on convolutional neural network. In: 2017 36th Chinese control conference (CCC), pp 11104–11109
Acknowledgments
This work is supported by Shandong Natural found (No. ZR2020MF033). We gratefully acknowledge the invaluable cooperation in preparing this paper.
Funding
This work was supported in part by the Science and Technology Development Plan of Shandong Province, China (No.2012G0020120), in part by the National Natural Resources Foundation, China (Nos. 61170078, 61472228).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflicts of interests/competing interests
There are no conflicts of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sha, J., Zeng, GL., Xu, ZF. et al. A light-weight and accurate pig detection method based on complex scenes. Multimed Tools Appl 82, 13649–13665 (2023). https://doi.org/10.1007/s11042-022-13771-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-13771-6