SheepFaceNet: A Speed–Accuracy Balanced Model for Sheep Face Recognition

Li, Xiaopeng; Zhang, Yichi; Li, Shuqin

doi:10.3390/ani13121930

Cited by 2 publications

(1 citation statement)

References 37 publications

(56 reference statements)

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“…Ref. [15] proposed a lightweight sheep face recognition model, SheepFaceNet, which achieved 97.75% recognition accuracy with 0.60 MB parameters. Ref.…”

Section: Introductionmentioning

confidence: 99%

VLDNet: An Ultra-Lightweight Crop Disease Identification Network

Zhang

Peng

et al. 2023

Agriculture

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Existing deep learning methods usually adopt deeper and wider network structures to achieve better performance. However, we found that this rule does not apply well to crop disease identification tasks, which inspired us to rethink the design paradigm of disease identification models. Crop diseases belong to fine-grained features and lack obvious patterns. Deeper and wider network structures will cause information loss of features, which will damage identification efficiency. Based on this, this paper designs a very lightweight disease identification network called VLDNet. The basic module VLDBlock of VLDNet extracts intrinsic features through 1 × 1 convolution, and uses cheap linear operations to supplement redundant features to improve feature extraction efficiency. In inference, reparameterization technology is used to further reduce the model size and improve inference speed. VLDNet achieves state-of-the-art model (SOTA) latency-accuracy trade-offs on self-built and public datasets, such as equivalent performance to Swin-Tiny with a parameter size of 0.097 MB and 0.04 G floating point operations (FLOPs), while reducing parameter size and FLOPs by 297 times and 111 times, respectively. In actual testing, VLDNet can recognize 221 images per second, which is far superior to similar accuracy models. This work is expected to further promote the application of deep learning-based crop disease identification methods in practical production.

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“…Ref. [15] proposed a lightweight sheep face recognition model, SheepFaceNet, which achieved 97.75% recognition accuracy with 0.60 MB parameters. Ref.…”

Section: Introductionmentioning

confidence: 99%

VLDNet: An Ultra-Lightweight Crop Disease Identification Network

Zhang

Peng

et al. 2023

Agriculture

Self Cite

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A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching

Xue,

Hou,

Xuan

et al. 2024

Animals

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As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

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SheepFaceNet: A Speed–Accuracy Balanced Model for Sheep Face Recognition

Cited by 2 publications

References 37 publications

VLDNet: An Ultra-Lightweight Crop Disease Identification Network

VLDNet: An Ultra-Lightweight Crop Disease Identification Network

A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching

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