DC-YOLO: A Behavior Detection Model for Hu Sheep Addressing Occlusion and Illumination Variations

doi:10.12133/j.smartag.SA202601021

Abstract

Abstract:

[Objective] In the livestock industry, animal behavior serves as a critical indicator for evaluating physiological health and providing environmental early warning. To address the challenges of severe multi-object occlusion, complex illumination, and class imbalance in intensive farming, a detail-contextual attention-YOLO (DC-YOLO) model was developed for the 24/7 automatic detection of daily behaviors in housed Hu sheep, thereby facilitating precision management. [Methods] For severe multi-object occlusion, an adaptive detail-contextual attention (DCAttention) mechanism was introduced to construct the DCAC3K2 module, and bounding box similarity soft-NMS (BS-NMS) was adopted to prevent incorrect removal of occluded bounding boxes. For complex illumination, a Light-Encoder was pre-trained on illumination conditions and its parameters were transferred to DC-YOLO. For class imbalance, a comprehensive classification-quality focal loss was designed to adaptively increase loss weights for occluded and minority-class samples. The dataset, collected through 24/7 surveillance at Hu sheep farms, comprised 505 annotated images depicting 5 daily behaviors: drinking, eating, lying, licking, and standing. [Results and Discussions] DC-YOLO achieved mAP@50 of 91.4%, improving by 7.8 percentage points over YOLOv12. Moreover, DC-YOLO had 2.29 M parameters, an 8.74% reduction compared to YOLOv12. On CPU, the inference time of DC-YOLO was reduced to 115.5 ms and the frame rate increased to 8.50 f/s, corresponding to improvements of 33.2% and 48.9%, respectively. [Conclusions] Experimental results demonstrate that DC-YOLO effectively mitigates detection challenges caused by severe occlusion, complex illumination, and class imbalance while maintaining high inference efficiency. Consequently, it provides effective technical support for tracking and analyzing behaviors in intensive Hu sheep farming.

Key words: Intensive livestock farming, Behavior detection, Hu sheep behavior, Attention Mechanism, YOLO, BS-NMS

CLC Number:

TP391.4

LI Xiaxi, JI Ronghua, CHANG Hongrui, ZHANG Suoxiang. DC-YOLO: A Behavior Detection Model for Hu Sheep Addressing Occlusion and Illumination Variations[J]. Smart Agriculture, 2026, 8(4): 127-147.

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References 40

[1]	OECD, FAO. OECD-FAO Agricultural Outlook 2025-2034[R/OL]. Paris: OECD Publishing; Rome: FAO, 2025[2025-11-05].
[2]	THARWAT M, ALKHERAIF A A, OIKAWA S. Production diseases in farm animals: A comprehensive and illustrated clinical, laboratory, and pathological overview[J]. Open Veterinary Journal, 2025, 15(1): 18-34.
[3]	HAYEK M N. The infectious disease trap of animal agriculture[J]. Science Advances, 2022, 8(44): eadd6681.
[4]	RICHMOND S E, WEMELSFELDER F, DE HEREDIA I B, et al. Evaluation of animal-based indicators to be used in a welfare assessment protocol for sheep[J]. Frontiers in Veterinary Science, 2017, 4: 210.
[5]	NECULAI-VALEANU A S, SANDULEANU C, POROSNICU I. From tradition to precision: Leveraging digital tools to improve cattle health and welfare[J]. Frontiers in Veterinary Science, 2025, 12: 1549512.
[6]	RIABOFF L, SHALLOO L, SMEATON A F, et al. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data[J]. Computers and Electronics in Agriculture, 2022, 192: 106610.
[7]	LAMANNA M, BOVO M, CAVALLINI D. Wearable collar technologies for dairy cows: A systematized review of the current applications and future innovations in precision livestock farming[J]. Animals, 2025, 15(3): 458.
[8]	HERLIN A, BRUNBERG E, HULTGREN J, et al. Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture[J]. Animals, 2021, 11(3): 829.
[9]	BEZEN R, EDAN Y, HALACHMI I. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms[J]. Computers and Electronics in Agriculture, 2020, 172: 105345.
[10]	WANG A, SUN Y, KORTYLEWSKI A, et al. Robust object detection under occlusion with context-aware CompositionalNets[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2020: 12642-12651.
[11]	KAUR R, SINGH S. A comprehensive review of object detection with deep learning[J]. Digital Signal Processing, 2023, 132: 103812.
[12]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2016: 779-788.
[13]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]// Computer Vision – ECCV 2016. Cham, Switzerland: Springer, 2016: 21-37.
[14]	BORGES OLIVEIRA D A, RIBEIRO PEREIRA L G, BRESOLIN T, et al. A review of deep learning algorithms for computer vision systems in livestock[J]. Livestock Science, 2021, 253: 104700.
[15]	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): 1137-1149.
[16]	CAI Z, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2018: 6154-6162.
[17]	HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2017: 2980-2988.
[18]	BOLYA D, ZHOU C, XIAO F, et al. YOLACT: Real-time instance segmentation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2019: 9156-9165.
[19]	CAO Z, HIDALGO G, SIMON T, et al. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(1): 172-186.
[20]	GONG C, ZHANG Y, WEI Y, et al. Multicow pose estimation based on keypoint extraction[J]. PLoS One, 2022, 17(6): e0269259.
[21]	GUO X, MA C, WANG C, et al. A sheep behavior recognition approach based on improved FESS-YOLOv8n neural network[J]. Animals, 2025, 15(6): 893.
[22]	WANG J, ZHAI Y, ZHU L, et al. PD-YOLO: A study of daily behavioural detection in housed sheep[J]. PLoS One, 2024, 19(11): e0313412.
[23]	HU T, YAN R, JIANG C, et al. Grazing sheep behaviour recognition based on improved YOLOV5[J]. Sensors, 2023, 23(10): 4752.
[24]	GU Z, ZHANG H, HE Z, et al. A two-stage recognition method based on deep learning for sheep behavior[J]. Computers and Electronics in Agriculture, 2023, 212: 108143.
[25]	YU Y, ZHU W, MA X, et al. Recognition of sheep feeding behavior in sheepfolds using fusion spectrogram depth features and acoustic features[J]. Animals, 2024, 14(22): 3267.
[26]	ZHANG J, QIAN S, CHEN J, et al. Calm Hu ram lambs assigned by temperament classification are healthier and have better meat quality than nervous Hu ram lambs[J]. Meat Science, 2021, 175: 108436.
[27]	JOCHER G, CHAURASIA A, QIU J. Ultralytics YOLOv8[CP/OL]. [2025-11-5].
[28]	LI X, WANG W, WU L, et al. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection[C]// Advances in Neural Information Processing Systems 33 (NeurIPS 2020. Vancouver, BC, Canada: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2020.
[29]	JOCHER G, QIU J. Ultralytics YOLO11[CP/OL]. [2025-11-5].
[30]	TIAN Y, YE Q, DOERMANN D. YOLOv12: Attention-Centric Real-Time Object Detectors[PP/OL]. arXiv: 2502.12524, 2025.
[31]	SHI D. TransNeXt: Robust foveal visual perception for vision transformers[C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2024: 17773-17783.
[32]	BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS—improving object detection with one line of code[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2017: 5562-5570.
[33]	WANG C Y, YEH I H, MARK LIAO H Y. YOLOv9: Learning what you want to learn using programmable gradient information[C]// Computer Vision – ECCV 2024. Cham, Germany: Springer, 2025: 1-21.
[34]	WANG A, CHEN H, LIU L, et al. YOLOv10: Real-Time End-to-End Object Detection[C]// Advances in Neural Information Processing Systems 37 (NeurIPS 2024). Red Hook, NY, USA: Curran Associates, Inc., 2024: 107984-108011.
[35]	LEI M Q, LI S Q, WU Y H, et al. YOLOv13: Real-time object detection with hypergraph-enhanced adaptive visual perception[PP/OL]. arXiv: 2506.17733, 2025.
[36]	ZHAO Y A, LV W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2024: 16965-16974.
[37]	LIU S L, LI F, ZHANG H, et al. DAB-DETR: Dynamic anchor boxes are better queries for DETR[PP/OL]. arXiv: 2201.12329, 2022.
[38]	ZHANG H, LI F, LIU S L, et al. DINO: DETR with improved DeNoising anchor boxes for end-to-end object detection[PP/OL]. arXiv: 2203.03605, 2022.
[39]	ZHANG Y F, SUN P Z, JIANG Y, et al. ByteTrack: Multi-object tracking by Associating every detection box[C]// Computer Vision-ECCV 2022: 17th European Conference. Cham, Switzerland: Springer, 2022: 1-21.
[40]	AHARON N, ORFAIG R, BOBROVSKY B Z. BoT-SORT: Robust associations multi-pedestrian tracking[PP/OL]. arXiv: 2206.14651, 2022.

Behavior ID	Typical behavior	Description
0	Drinking	Standing with limbs outstretched and drinking water while looking down at the pipe
1	Eating	Standing with limbs extended and head lowered to eat in front of the feeding trough
2	Lying	Body resting on the ground， with limbs bent or tucked
3	Licking	Standing with limbs outstretched and licking the salt brick
4	Standing	Standing on all limbs with the body supported， and not actively engaged in eating， drinking， or licking

Configuration Item	Value
CPU	Intel（R） Xeon（R） Platinum 8352 V CPU @ 2.10 GHz
GPU	NVIDIA GeForce RTX 4090
OS	Ubuntu 20.04
Programming language	Python 3.8
Deep learning framework	PyTorch 2.0.0
Version of CUDA	CUDA 11.8
System RAM	120 GB

Model	DCAC3K2	CQFL	BS-NMS	A2C2f	P/%	R/%	mAP@50/%	mAP@50：95/%	Parameters/M	t _infer/ms	t _post/ms
YOLOv12	×	×	×	√	81.6	78.5	83.6	64.9	2.51	172.8	0.37
1	√	×	×	√	79.0	87.7	86.7	66.0	2.17	130.3	0.31
2	×	√	×	√	85.9	74.5	85.5	66.9	2.51	165.3	0.39
3	×	×	√	√	83.7	82.8	87.6	68.6	2.51	168.2	1.60
4	√	√	×	√	86.5	86.2	88.0	71.5	2.17	143.2	0.37
5	√	√	√	√	91.0	86.8	90.1	73.8	2.17	133.5	1.74
6	√	√	×	×	85.9	86.1	88.9	73.5	2.29	163.7	1.71
DC-YOLO	√	√	√	×	91.0	86.6	91.4	75.9	2.29	115.5	1.69

Model	Pre-training method	mAP/%				P/%	R/%
Model	Pre-training method	daytime natural	nighttime	abnormal lighting	All	P/%	R/%
YOLOv11	YOLOv11n	93.0	82.9	84.5	87.3	88.1	83.9
	Random initial weights	91.9	82.1	83.9	86.8	83.4	81.8
	Light-Encoder	92.8	83.4	88.4	88.9	84.9	77.6
YOLOv12	YOLOv12n	92.5	80.8	78.7	84.6	79.5	82.7
	Random initial weights	91.8	80.1	77.3	83.6	81.6	78.5
	Light-Encoder	92.4	82.9	80.1	85.7	77.4	85.9
DC-YOLO （ours）	YOLOv11n	94.1	84.3	87.4	90.6	90.0	86.1
	YOLOv12n	94.0	84.4	86.9	90.5	83.7	85.1
	Random initial weights	93.1	83.7	85.9	89.9	83.4	87.6
	Light-Encoder	93.9	85.1	89.3	91.4	91.0	86.6

Model	P /%	R /%	mAP /%	Parameters/M	Computational/GFLOPs	FPS/（f/s）
YOLOv8	81.4	81.6	85.8	2.69	6.8	8.56
YOLOv9	83.5	78.0	84.3	2.43	6.4	7.03
YOLOv10	79.4	76.4	82.2	2.27	6.5	7.51
YOLOv11	83.4	81.8	86.8	2.59	6.4	7.58
YOLOv12	81.6	78.5	83.6	2.51	6.9	5.71
YOLOv13	77.9	83.8	83.9	2.45	6.1	4.29
DINO	62.7	67.6	68.7	47.00	279.0	2.58
DAB-DETR	43.2	51.7	50.4	43.00	195.0	2.52
RT-DETR	63.1	73.6	71.4	42.77	130.5	1.21
DC-YOLO	91.0	86.6	91.4	2.29	6.2	8.50