Behavior Detection Algorithm for Caged White-feather Broilers Based on Improved YOLO Detection Framework

XIA Yuantian, KOU Xupeng, XUE Hongcheng, LI Lin

Abstract

In large-scale broiler farms, the behavior of broilers is usually observed and analyzed by feeders or professional veterinarians to determine their health status and breeding environment status. However, this method is time-consuming and subjective. In addition, in caged environments, due to the high density of chickens and serious mutual occlusion, the visual features of behavior are not obvious, and traditional detection algorithms cannot accurately identify the behavior characteristics of chickens. Therefore, an improved object detection algorithm for behavior detection of caged white-feather broilers was proposed. The proposed algorithm consisted of two modules: multi-scale detail feature fusion module (MDF) and object relation inference module (ORI). The multi-scale detail feature module fully utilized and extracted the multi-scale detail features contained in the shallow feature maps of the feature extraction network, and integrated them into the corresponding feature maps responsible for detection at the corresponding scale, achieving effective transmission and supplementation of detail features. The relational reasoning module fully utilized the positional relationships between objects for inference and judgment, enabling the model to more fully utilize the potential relationships between objects to assist in detection. To verify the effectiveness of the proposed algorithm, a large number of comparative experiments on both authoritative public datasets in the field of object detection and self-built behavior detection datasets in real large-scale caged white-feather broiler breeding environments was conducted. The experimental results showed that the proposed improved algorithm achieved the best detection accuracy compared with other state-of-the-art models, both in the COCO dataset and the self-built dataset. For the detection of behaviors such as feeding, drinking, moving, and opening the mouth, which were crucial for the health status of broiler chickens, the algorithm achieved accuracy rates of 99.6%, 98.7%, 99.2%, and 98.3% respectively.

 

Keyword: white-feather broilers ; behavior recognition ; object detection ; multi-scale detail feature fusion module ; relation inference module

 

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PEDEN R S E, TURNER S P, BOYLE L A, et al. The translation of animal welfare research into practice: the case of mixing aggression between pigs[ J ]. Applied Animal Behaviour Science, 2018, 204; 1 -9.

MAGHSOUDI О H, TABRIZI A V, ROBERTSON B, et al. Honeybee detection and pose estimation using convolutional neural networks [С] // Proceedings of the 2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017 : 69 -73.

ZHAI Yahong, WANG Jie, XU Longyan,et al. Behavior recognition of domesticated sheep based on improved YOLO v5n [J] . Transactions of the Chinese Society for Agricultural Machinery, 2024,55(4) :231 -240. (in Chinese)

WANG Wang, WANG Fushun, ZHANG Weijin,et al. Sheep behavior recognition method based on improved OLO v8s[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024,55(7) ;325 -335,344. (in Chinese)

YUAN Hongbo, CAO Runliu, CHENG Man,et al. Fusion of Res3D, BiLSTM and attention mechanism for sheep behavior recognition method! [J]. Transactions of the Chinese Society for Agricultural Machinery, 2024,55(4) ;221 -230. ( in Chinese)

LI Enze, WANG Kejian, SI Yongsheng,et al. Cow behavior recognition method based on improved ConvNeX [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2024,55(5) ;282 -289,404. (in Chinese)

DUAN Qingling, ZHAO Zhiqing, JIANG Tao,et al. Behavior recognition method of beef cattle based on SNSS- YOLO v7 [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2023 ,54( 10) :266 -274,347. (in Chinese)

LI Lin, BAI Zhao, DIAO Lei,et al. Video detection and counting method of east Asian migratory locusts based on К — SSI) — F [J ]. Transactions of the Chinese Society for Agricultural Machinery,2021 ,52(Supp. ) :261 -267. (in Chinese)

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [ С ]// Proceedings of the Conference on Computer Vision and Pattern Recognition, 2014:580 -587.

GIRSHICK R. Fast R — CNN[C] //Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440 - 1448.

REN S, HE K, GIRSHICK R, et al. Faster R —CNN; towards real-time object detection with region proposal networks [J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6) : 1 137 - 1149.

DAI J, LI Y, HE K, et al. R- FCN: object detection via regionbased fully convolutional networks [С] // Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS'16) , 2016; 379 -387.

UIJLINGS J R, SANDE К E, GEVERS T, et al. Selective search for object recognition[ J]. International Journal of Computer Vision, 2013, 104(2) : 154 - 171.

REDMON J, FARHADI A. M)L09000: better, faster, stronger [ С ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017 : 6517 -6525.

REDMON J, FARHADI A. Yolov3; an incremental improvement [ J ]. arXiv Preprint, arXiv; 1804.02767,2018. BOCHKOVSKIY A, WANG С Y, LIAO II 'i M, et al. Yolov4: optimal speed and accuracy of object detection [ J ]. arXiv Preprint, arXiv; 2004.10934,2020.

WANG С Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN [C] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 1571 - 1580.

JJOCHERG, NISHIM UR A K, MINEEVA T, et al. YOLO v5[ EB/OL]. (2022 -06-26) [2021 -06 -02]. https; //github. com/ultralytics/yolov5.

CHUYI L, LULU L, HONGLIANG J, et al. YOLO v6; a single-stage object detection framework for industrial applications [J]. arXiv Preprint, arXiv:2209.02976,2022.

WANG С Y, BOCHKOVSKIY A, et al. Yolov7 ; trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [J]. arXiv Preprint, arXiv: 2207. 02696 , 2022.

LIU W, ANGUELOV D, ERHAN D, et al. SSD; single shot multibox detector [ С ] // European Conference on Computer Vision, 2016: 21 -37.

FU С Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector[J]. arXiv Preprint, arXiv: 1701. 06659, 2017.

EVERINGHAM M, VAN G L, WILLIAMS С К, et al. The pascal visual object classes (voc) challenge [J ]. International Journal of Computer Vision, 2010, 88(2) : 303 -338.

LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO; common objects in context [ С ] European Conference on Computer Vision, 2014; 740 -755.


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