Sheep face image dataset and DT-YOLOv5s for sheep breed recognition

Guo, Ying; Yu, Zhihong; Hou, Zhenjie; Zhang, Wenguang; Qi, Guimei

doi:10.1016/j.compag.2023.108027

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…The experimental results showed that this enhanced algorithm achieved an mAP (mean average precision) of 97.8%. In 2023, Guo et al [21] aimed to enhance the robustness of their model. They transferred the parameters learned from the complex YOLOv5x to the lightweight YOLOv5s.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

Hao,

Sun,

Xuan

et al. 2024

Agriculture

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We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

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Section: Introductionmentioning

confidence: 99%

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

Hao,

Sun,

Xuan

et al. 2024

Agriculture

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“…Subsequently, Xu et al [29] improved GFPN and applied it to the YOLO network, increasing accuracy by 1.4%. Guo et al [30] combined knowledge distillation strategy in the YOLOv5s model and achieved an accuracy of 94.67% on a self-made dataset, which is 4.83% higher than the original model. Yang et al [31] constructed a lightweight model method based on backbone replacement, sparse training and knowledge distillation techniques, which method reduces parameters and volume, but AP also decreases by 2.7%.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices

Sun,

Li,

et al. 2024

Preprint

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In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

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High-similarity sheep face recognition method based on a Siamese network with fewer training samples

Zhang,

Xuan,

et al. 2024

Computers and Electronics in Agriculture

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Sheep face image dataset and DT-YOLOv5s for sheep breed recognition

Cited by 10 publications

References 20 publications

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices

High-similarity sheep face recognition method based on a Siamese network with fewer training samples

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