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, an efficient model is developed for the 24/7 automatic detection of daily behaviors in housed Hu sheep, thereby facilitating precision management. The DCAttention-YOLO (DC-YOLO) model is proposed. [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 (CQFL) 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 increases 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

CLC Number:

TP391.41

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, doi: 10.12133/j.smartag.SA202601021.

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Behavior ID	Typical behavior	Description
0	Drinking	Standing with limbs outstretched and drink 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 lick 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	Python3.8
Deep learning framework	PyTorch2.0.0
Version of CUDA	CUDA11.8
System RAM	120 GB

Model	DCAC3K2	CQFL	BS-NMS	A2C2f	P/%	R/%	mAP50/%	mAP50：95/%	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	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