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REVIEW 4 major objections 5 minor 29 references

Sheep Facial Pain Assessment Under Weighted Graph Neural Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that a weighted graph neural network over nine sheep facial landmarks, fed by a lightweight YOLOv8n detector, can assess pain level with 92.71% training and 91.96% testing accuracy.

desk verdict The new sheep facial landmark dataset is a genuine asset, but the headline 92.71% accuracy is not interpretable because the paper never shows that the ground-truth labels are independent of the SPFES aggregation formula the WGNN reproduces. read the letter →

arxiv 2506.01468 v1 pith:VZBVPOMF submitted 2025-06-02 cs.CV

Alam Noor , Luis Almeida , Mohamed Daoudi , Kai Li , Eduardo Tovar This is my paper
classification cs.CV
keywords sheepfacialpainassessmentweightedgraphneuralnetworklandmarkdetectionExpressionScaleYOLOv8nlevelpredictionanimalwelfaremonitoringobject
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that sheep pain can be read automatically from facial landmarks by linking nine part-level expressions (ears, eyes, nose, cheeks, lips and jaw) with a weighted graph neural network that clusters related parts and aggregates their pain scores. The proposed model is paired with a lightweight YOLOv8n detector, and the authors report 92.71% training and 91.96% testing accuracy on a new sheep facial landmarks dataset built on the Sheep Facial Expression Scale (SPFES). If these numbers hold beyond the paper's single 80/20 split, the approach would give farmers and veterinarians a cheap, non-invasive way to monitor pain continuously in natural settings.

What carries the argument

The central object is a weighted graph neural network (WGNN) whose nodes are the nine SPFES facial part expressions and whose edges connect parts that are anatomically close or share similar pain scores. The carried mechanism is message passing ($M_i^{(k)} = \sum_{j \in N(i)} \psi_j^{(k-1)}$; $\psi_i^{(k)} = F(\sigma)(W^{(k)} M_i^{(k)} + b^{(k)})$) followed by weighted-average clustering within each cluster ($S_j = \sum_{i \in C_j} w_i p_i / \sum_{i \in C_j} w_i$) and a weighted sum over clusters ($T_p = \sum_{j=1}^o w_j S_j$), normalized to a 0-100% pain score. The YOLOv8n detector supplies the per-part pain scores $p_i$ that initialize the node features, so the graph's accuracy is bounded by detector quality.

What would settle it

Take a held-out collection of sheep face images from several farms with different breeds, lighting, and camera angles, have veterinarians assign SPFES pain scores, and compare WGNN+YOLOv8n predictions against those ground-truth scores. A large accuracy drop, or a finding that changing the hand-assigned SPFES weights in Eq. (5) changes the reported accuracy by more than a few points, would show the result depends on the particular split and weight choices.

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Extended reading notes

Core claim

The central claim is that pain-level assessment improves when the face is treated not as one whole image but as a graph of nine facial part expressions, where nodes carry per-part pain scores from a detector and edges encode anatomical proximity and similarity of pain. The WGNN infers the optimal parse graph, passes messages between neighboring nodes, clusters parts into pain-related groups, and computes a weighted total pain score normalized to 0-100%. Compared with SVM, EfficientNetB5, CCVT, and CNN on the new sheep facial landmarks dataset, the WGNN with YOLOv8n reaches the highest accuracy (92.71% train, 91.96% test).

Load-bearing premise

The load-bearing premise is that the detector's per-face-part pain scores are accurate enough to serve as graph node features and that the hand-assigned SPFES weights correctly reflect each part's contribution to pain; the authors acknowledge that detection errors propagate directly into the WGNN's predictions.

Editorial extensions

If this is right

  • Farmers and veterinarians could obtain an automated, continuous pain score from ordinary face images or UAV footage without handling the sheep.
  • The new sheep facial landmarks dataset, with nine facial expressions across five facial parts, provides a reusable benchmark for later detection and pain-assessment work.
  • The lightweight YOLOv8n detector (6.3M parameters, 1.9 ms inference per image) makes on-board or embedded deployment feasible.
  • Clustering facial parts by shared pain scores yields an interpretable total pain score in the 0-100% range, rather than just a categorical label.
  • The WGNN framework exceeds the reported accuracy of SVM, EfficientNetB5, CCVT, and CNN on this dataset.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the same weighted-cluster aggregation were applied to other livestock species with standardized facial expression scales, the approach might transfer without retraining the graph, provided per-part pain scores are available.
  • Because the weighted average in Eq. (5) is transparent, the model could be extended to output per-facial-part pain contributions to a veterinarian, not just a single score.
  • The single 80/20 split and five-day monitoring period leave open whether the 92% accuracy generalizes across breeds, farms, and lighting; a cross-farm evaluation with multiple annotators would be the natural next test.
  • A weaker detector than YOLOv8n would likely lower the ceiling, since the paper itself states that detection performance determines the WGNN model's performance.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a Weighted Graph Neural Network (WGNN) for assessing pain levels in sheep from facial landmarks. The pipeline combines a lightweight YOLOv8n detector that localizes nine facial part expressions (ears, eyes, nose, etc.) with a graph-based model that clusters these parts and aggregates per-part pain scores using weights derived from the Sheep Facial Expression Scale (SPFES). The authors introduce a new sheep facial landmark dataset and report that WGNN achieves 92.71% training accuracy and 91.96% testing accuracy, outperforming SVM, EfficientNetB5, CCVT, and CNN. The paper also reports detector mAP values for eight object detectors, with YOLOv8n giving the best mAP of 59.30%.

Significance. If the reported accuracy were valid, the paper would offer a practical application of GNNs for automated animal pain assessment, a topic of increasing interest for precision livestock farming. The proposed dataset could also be a useful resource. However, the current manuscript does not provide sufficient experimental detail or validation to support the central accuracy claim: the ground-truth labeling protocol is unspecified, the graph inference in Eq. (2) is not implemented or described, the dataset size is missing, and the evaluation lacks statistical rigor. These gaps make the claimed 92.71% accuracy not interpretable as a measure of pain-assessment ability. The significance is therefore conditional: the idea is promising, but the evidence presented is inadequate.

major comments (4)
  1. [§IV.1 and §III, Eqs. (5)-(7)] The ground-truth labeling protocol for the overall pain level is never described. Section IV.1 states that the dataset is labeled according to SPFES parameters, where each facial part expression is categorized as 0, 1, or 2. In Section III, Eqs. (5)-(7) compute the final pain score as a weighted average of these same p_i values using SPFES weights w_i, followed by a weighted sum over clusters and normalization. If the image-level ground-truth pain label was generated by applying the same SPFES weighted-aggregation rule to the manually annotated part scores, then the reported 92.71% accuracy would only measure how accurately YOLOv8n reproduces the part-level scores; the WGNN's clustering and message passing would be irrelevant. The paper must specify the complete annotation protocol, report the numerical values of w_i and w_j, and demonstrate that the ground-truth pain labels are independent of the aggregation formula in Eqs. (5)-(7). Without this, the headline accuracy cannot be taken as evidence of pain-assessment skill.
  2. [§III, Eq. (2) and §IV] The parse-graph inference in Eq. (2) is presented as the core of the proposed method, but the experiments do not describe how g* is computed or whether it is optimized at all. There is no explanation of the probability distribution Pd, the number of clusters O, the edge-construction rule (physical proximity vs. pain-score similarity), or the GNN architecture (number of layers, hidden dimensions, training loss). The experimental section only states that cross-entropy loss is used to cluster facial landmarks. As a result, the method is not reproducible, and it is unclear whether the learned graph component contributes anything beyond the weighted-average aggregation in Eqs. (5)-(7). Please provide the implementation details, or if Eq. (2) was not actually used in the experiments, revise the method description accordingly.
  3. [§IV, Table II and Fig. 5] The evaluation is missing basic statistical information. The dataset size is never reported; only a single 80/20 split is used, with no repeated runs, confidence intervals, or per-class results. Training and testing accuracies are given without a confusion matrix or class-wise breakdown. This is particularly problematic because the detector mAP is only 59.30% (Table I); the paper does not explain how an overall pain-level accuracy of 91.96% is achieved when part detection has low mAP, nor does it define the accuracy metric (exact match of the normalized score? thresholded pain class?). The SOTA comparison in Table II also lacks essential details: were SVM, EfficientNetB5, CCVT, and CNN evaluated on the same detected part-level features, on raw images, or with the same post-processing? Without matched input representations and evaluation protocols, the comparison does not support the claim of superiority.
  4. [§IV.6 and Fig. 7] The total pain score (TPS) over five days is presented in Fig. 7 without specifying the number of animals, number of images, error bars, or statistical tests. The relationship between the TPS computed by the proposed model and the TPS defined in McLennan et al. [13] is not explained, so the reader cannot tell whether the plotted trend is the model's output or the ground truth. Please clarify what is shown and how it supports the claim of long-term pain monitoring.
minor comments (5)
  1. [Throughout] There are several typographical errors and inconsistencies, e.g., 'EfficeintNet' should be 'EfficientNet', 'UA Vs' should be 'UAVs', and 'Rethinking transformation' appears to be augmentation but is not clearly defined. Please proofread carefully.
  2. [§IV.1] The dataset description does not state the total number of images, the number of sheep, or the class distribution for the three pain categories. This information is necessary for interpreting the reported accuracies and for anyone wishing to use the dataset.
  3. [§III, Eq. (1)] The feature vector ψ_i^(0) in Eq. (1) is described as [p_i, FPT_i], but FPT (facial part expression type) is not defined clearly, and it is unclear how this feature is encoded (one-hot? scalar?). Please clarify.
  4. [§IV.3 and Table I] Table I reports precision and recall values, but the definitions of precision and recall for multi-class landmark detection are not specified. Given the low mAP values, it would be helpful to show per-class results and the confidence threshold used.
  5. [§IV.4] The ablation study in Fig. 5 reports 'accuracy' for four YOLO models combined with WGNN, but the caption and text do not define whether this is training accuracy, validation accuracy, or the final pain-level accuracy. Please provide a consistent metric definition.

Circularity Check

1 steps flagged · score 7.0 of 10

The WGNN's final pain score is a weighted average of the same per-part SPFES scores used to label the dataset, so the headline accuracy may measure reproduction of the annotation formula rather than pain-assessment skill.

  1. self definitional [Section III, Eqs. (5)–(7); Section IV.1 and IV.6]
    "Sj = P i∈Cj wi · pi / P i∈Cj wi (5), while, wi is the weight assigned to each part expressions pi based on the SPFES. ... Tp = P o j=1 wj · Sj (6) ... NPS = (Tp/Tmax) × 100 (7). Section IV.1: 'We developed a dataset with a bounding box of sheep facial landmarks using high-resolution images from well-known sources according to the SPFES standard [12], [13].' Section IV.6: 'we test the proposed WGNN model with YOLOv8n object detection models to find the total pain score (TPS) as given in [13].'"

    The predicted total pain Tp is defined in Eqs. (5)–(7) as a weighted average of the detector-provided per-part pain levels p_i, with weights w_i and w_j 'based on the SPFES.' The ground-truth dataset is labeled 'according to the SPFES standard [12], [13]', and the paper tests WGNN by finding the TPS 'as given in [13]'. If the SPFES ground-truth label is computed from the same p_i annotations and the same SPFES weights, then Eq. (6) is exactly the label-generation formula, and any detector that recovers p_i correctly is scored correct regardless of whether the GNN clustering or message passing contributes anything.

full rationale

The central accuracy claim rests on an evaluation loop that the paper never breaks: the dataset is annotated with SPFES part-level scores, the model's total pain score is a weighted average of those same part-level scores with SPFES weights, and TPS is validated 'as given in [13]' — the same SPFES source used to assign the weights. Because the paper does not describe an independent image-level pain label or report the weights and label-generation rule, the reported accuracy is consistent with the model simply reproducing the SPFES aggregation formula from the YOLO part detections. This is a self-definitional concern about the headline claim, not a matter of tuning or benchmark selection. Self-citations (e.g., [14], [16]) are not load-bearing here; the circularity is in the relationship between Eqs. (5)–(7) and the SPFES-based labels. Score 7 rather than 8–10 because the paper does not explicitly state the exact ground-truth formula; however, all quoted elements align and no evidence of label independence is provided.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the SPFES labeling scheme, the detector's p_i outputs, and a set of unreported weights and hyperparameters. The probabilistic parse-graph model in Eq. (2) is stated but never implemented or validated, so the effective assumptions are narrower than the prose claims.

free parameters (4)
  • w_i (per-part-expression SPFES weights) = not reported
    Used in Eq. (5) to weight each facial expression's pain score; the paper says they are based on SPFES but gives no values or derivation, so they may be hand-tuned to the dataset.
  • w_j (cluster weights) = not reported
    Used in Eq. (6) to weight each cluster's aggregated pain score; no values or justification are provided.
  • T_max (maximum possible pain score) = not reported
    Normalization constant in Eq. (7) that scales the final pain score to 0-100 percent; its value is not specified.
  • Number of pain clusters O = not reported
    The GNN clusters nodes into O clusters, but the number of clusters and the clustering method are never stated in the experimental section.
assumptions (4)
  • domain assumption SPFES annotations are a valid and reliable ground truth for sheep pain
    Section IV.1 labels images according to SPFES; if the labels are inconsistent or wrong, all accuracy numbers are meaningless.
  • domain assumption YOLOv8n per-part pain scores p_i are accurate enough to serve as graph node features
    Section IV.7 states detector performance determines WGNN performance, and Fig. 8 shows failure cases; the 59.30% mAP is modest.
  • ad hoc to paper The parse-graph objective in Eq. (2) is realizable and is what the experiments optimize
    Eq. (2) is presented as the core inference rule, but the paper never describes how P_d(Ng,Eg|...) and P_d(Og|...) are parameterized or optimized; the experiments instead rely on the weighted average in Eqs. (5)-(7).
  • ad hoc to paper Edges built from physical proximity and pain-score similarity are sufficient
    Section III assumes close facial parts and similar pain scores should be linked; no ablation over alternative graph structures is provided.

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Cite this review

Pith. "Pith review of Sheep Facial Pain Assessment Under Weighted Graph Neural Networks." pith.science (2026). https://pith.science/paper/VZBVPOMF

@misc{pith2026250601468,
  author       = {Pith},
  title        = {Pith review of: Sheep Facial Pain Assessment Under Weighted Graph Neural Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VZBVPOMF}},
  note         = {Machine review of arXiv:2506.01468}
}
read the original abstract

Accurately recognizing and assessing pain in sheep is key to discern animal health and mitigating harmful situations. However, such accuracy is limited by the ability to manage automatic monitoring of pain in those animals. Facial expression scoring is a widely used and useful method to evaluate pain in both humans and other living beings. Researchers also analyzed the facial expressions of sheep to assess their health state and concluded that facial landmark detection and pain level prediction are essential. For this purpose, we propose a novel weighted graph neural network (WGNN) model to link sheep's detected facial landmarks and define pain levels. Furthermore, we propose a new sheep facial landmarks dataset that adheres to the parameters of the Sheep Facial Expression Scale (SPFES). Currently, there is no comprehensive performance benchmark that specifically evaluates the use of graph neural networks (GNNs) on sheep facial landmark data to detect and measure pain levels. The YOLOv8n detector architecture achieves a mean average precision (mAP) of 59.30% with the sheep facial landmarks dataset, among seven other detection models. The WGNN framework has an accuracy of 92.71% for tracking multiple facial parts expressions with the YOLOv8n lightweight on-board device deployment-capable model.

Figures

Figures reproduced from arXiv: 2506.01468 by the authors.

Figure 1
Figure 1. The proposed WGNN model workflow for identifying sheep facial landmarks involves mobile devices identifying the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Sheep facial units statistical approach with bounding [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. According to the sheep facial dataset, the precision [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: Classification performance (PR) of all detection models using precision (red) and recall (green) for the sheep facial [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 4
Figure 4. Figure 4: mAP for different facial landmark detection models [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Accuracy after combining WGNN with the four dif [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Evaluation results with WGNN applied after the object detection model, using the facial landmarks to find connections [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Correlation between the pain level and the number of [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Cases of incorrect sheep facial landmark detections [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

  1. [13]

    K. M. McLennan, C. J. Rebelo, M. J. Corke, M. A. Holmes, M. C. Leach, and F. Constantino-Casas. Development of a facial expression scale using footrot and mastitis as models of pain in sheep. Applied Animal Behaviour Science , 176:19–26, 2016

    work page 2016

  2. [1]

    M. Y . Ayub, A. Hussain, M. F. U. Hassan, B. Khan, F. A. Khan, D. Al- Jumeily, and W. Khan. A non-restraining sheep activity detection and surveillance using deep machine learning. In 2023 16th International Conference on Developments in eSystems Engineering (DeSE) , pages 66–72, 2023

    work page 2023

  3. [2]

    C. T. Bati and G. Ser. Improved sheep identification and tracking algorithm based on yolov5 + sort methods. Signal, Image and Video Processing, 18(10):6683–6694, 2024

    work page 2024

  4. [3]

    Z. Cai, M. Chen, R. Jing, and Y . Zhang. Sheep face detection and disease prevention based on convolutional neural network. In Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering, EITCE ’23, page 1501–1505, New York, NY , USA, 2024. Association for Computing Machinery

    work page 2023

  5. [4]

    Z. Feng, M. Karaskova, and M. Mahmoud. Open-sheep-face: A comprehensive application for sheep face analysis and pain estimation. In 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) , pages 1–3, 2023. 8

    work page 2023

  6. [5]

    Z. Gu, H. Zhang, Z. He, and K. Niu. A two-stage recognition method based on deep learning for sheep behavior. Computers and Electronics in Agriculture, 212:108143, 2023

    work page 2023

  7. [6]

    Y . Guo, Z. Yu, Z. Hou, W. Zhang, and G. Qi. Sheep face image dataset and dt-yolov5s for sheep breed recognition. Computers and Electronics in Agriculture, 211:108027, 2023

    work page 2023

  8. [7]

    M. Hao, Q. Sun, C. Xuan, X. Zhang, M. Zhao, and S. Song. Lightweight small-tailed han sheep facial recognition based on im- proved ssd algorithm. Agriculture, 14(3), 2024

    work page 2024

Show all 29 references
  1. [8]

    G. M. S. Himel, M. M. Islam, and M. Rahaman. Utilizing efficientnet for sheep breed identification in low-resolution images. Systems and Soft Computing, 6:200093, 2024

    2024

  2. [9]

    Hitelman, Y

    A. Hitelman, Y . Edan, A. Godo, R. Berenstein, J. Lepar, and I. Ha- lachmi. Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture , 194:106713, 2022

    2022

  3. [10]

    N. A. Kelly, B. M. Khan, M. Y . Ayub, A. J. Hussain, K. Dajani, Y . Hou, and W. Khan. Video dataset of sheep activity for animal behavioral analysis via deep learning. Data in Brief , 52:110027, 2024

    2024

  4. [11]

    X. Li, Y . Xiang, and S. Li. Combining convolutional and vision transformer structures for sheep face recognition. Computers and Electronics in Agriculture, 205:107651, 2023

    2023

  5. [12]

    Y . Lu, M. Mahmoud, and P. Robinson. Estimating sheep pain level using facial action unit detection. In 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) , pages 394–399, 2017

    2017

  6. [14]

    A. Noor, M. J. Corke, and E. Tovar. Sheep health behavior analysis in machine learning: A short comprehensive survey. Smart Agricultural Technology, 6:100366, 2023

    2023

  7. [15]

    A. Noor, K. Li, A. Ammar, A. Koubaa, B. Benjdira, and E. Tovar. A hybrid deep learning model for uavs detection in day and night dual visions. In 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI) , pages 221–231, 2021

    2021

  8. [16]

    A. Noor, Y . Zhao, A. Koubaa, L. Wu, R. Khan, and F. Y . Abdalla. Automated sheep facial expression classification using deep transfer learning. Computers and Electronics in Agriculture , 175:105528, 2020

    2020

  9. [17]

    Y . Pang, W. Yu, Y . Zhang, C. Xuan, and P. Wu. An attentional residual feature fusion mechanism for sheep face recognition. Scientific Reports, 13(1):17128, 2023

    2023

  10. [18]

    S. Qi, W. Wang, B. Jia, J. Shen, and S.-C. Zhu. Learning human- object interactions by graph parsing neural networks. In V . Ferrari, M. Hebert, C. Sminchisescu, and Y . Weiss, editors, Computer Vision – ECCV 2018 , pages 407–423, Cham, 2018. Springer International Publishing

    2018

  11. [19]

    Sarantinoudis, G

    N. Sarantinoudis, G. Arampatzis, K. P. Valavanis, and N. Tsourveloudis. Unmanned aerial vehicles and livestock management: An application in western crete. In 2023 International Conference on Unmanned Aircraft Systems (ICUAS) , pages 159–166, 2023

    2023

  12. [20]

    Sarwar, A

    F. Sarwar, A. Griffin, S. U. Rehman, and T. Pasang. Detecting sheep in uav images. Computers and Electronics in Agriculture, 187:106219, 2021

    2021

  13. [21]

    A. E. O. Smith, C. Doidge, T. Knific, F. Lovatt, and J. Kaler. The tales of contradiction: A thematic analysis of british sheep farmers’ perceptions of managing sheep scab in their flocks. Preventive Veterinary Medicine, 227:106194, 2024

    2024

  14. [22]

    Xinyu, T

    Z. Xinyu, T. Zhenzhen, Y . Wei, L. Lei, and W. Jihua. Dcam-net: Sheep gender identification network based on dilated convolutional attention module. In 2023 13th International Conference on Information Technology in Medicine and Education (ITME), pages 283–287, 2023

    2023

  15. [23]

    J. Xue, Z. Hou, C. Xuan, Y . Ma, Q. Sun, X. Zhang, and L. Zhong. A sheep identification method based on three-dimensional sheep face reconstruction and feature point matching. Animals, 14(13), 2024

    2024

  16. [24]

    X. Yan, C. Yang, B. Shi, L. Ao, and S. Ma. A goat facial recognition approach based on an enhanced yolov8s and keypoint affine transfor- mation. In 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL) , pages 565–571, 2024

    2024

  17. [25]

    Zhang, Y

    H. Zhang, Y . Ma, X. Wang, R. Mao, and M. Wang. Lightweight real- time detection model for multi-sheep abnormal behaviour based on yolov7-tiny. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 4191–4196, 2023

    2023

  18. [26]

    Zhang, C

    X. Zhang, C. Xuan, Y . Ma, H. Liu, and J. Xue. Lightweight model-based sheep face recognition via face image recording channel. Journal of Animal Science , 102:skae066, 03 2024

    2024

  19. [27]

    Zhang, C

    X. Zhang, C. Xuan, Y . Ma, H. Su, and M. Zhang. Biometric facial identification using attention module optimized yolov4 for sheep. Computers and Electronics in Agriculture , 203:107452, 2022

    2022

  20. [28]

    Zhang, C

    X. Zhang, C. Xuan, Y . Ma, Z. Tang, and X. Gao. An efficient method for multi-view sheep face recognition. Engineering Applications of Artificial Intelligence, 134:108697, 2024

    2024

  21. [29]

    Zhang, C

    X. Zhang, C. Xuan, J. Xue, B. Chen, and Y . Ma. Lsr-yolo: A high- precision, lightweight model for sheep face recognition on the mobile end. Animals, 13(11), 2023. 9

    2023

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Reviewed August 7, 2026 · model on record in the stance chip above.