Pig Face Identification Based on Improved Alexnet Model

Yan, Hongwen; Cui, Qingliang; Liu, Zhenyu

doi:10.35633/inmateh-61-11

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…To verify the efectiveness of the model proposed in this study, the solid-color cow face dataset was trained on the pig face recognition model proposed by Yan et al [28] and the sheep face recognition model proposed by Abu Jwade et al [27]. Te experimental results are shown in Table 5.…”

Section: Experimental Results and Analysismentioning

confidence: 98%

Facial Recognition of Cattle Based on SK-ResNet

Gong

Pan

Chen

et al. 2022

Scientific Programming

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With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock.

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Section: Experimental Results and Analysismentioning

confidence: 98%

Facial Recognition of Cattle Based on SK-ResNet

Gong

Pan

Chen

et al. 2022

Scientific Programming

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“…The larger the value of AP, the better the detection model is. The formula for calculating AP is shown in Equation (3).…”

Section: Evaluation Indicators and Experimental Environment 231 Evalu...mentioning

confidence: 99%

“…In addition, they require manual registration, which is both time-consuming and prone to error [2]. Radio frequency identification (RFID) is widely used in individual livestock identification [3]. However, it is more costly than traditional methods and is susceptible to interference.…”

Section: Introductionmentioning

confidence: 99%

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Yang

et al. 2022

Agriculture

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Sheep face recognition models deployed on edge devices require a good trade-off between model size and accuracy, but the existing recognition models cannot do so. To solve the above problems, this paper combines Mobilenetv2 with Vision Transformer to propose a balanced sheep face recognition model called MobileViTFace. MobileViTFace enhances the model’s ability to extract fine-grained features and suppress the interference of background information through Transformer to distinguish different sheep faces more effectively. Thus, it can distinguish different sheep faces more effectively. The recognition accuracy of 96.94% is obtained on a self-built dataset containing 5490 sheep face photos of 105 sheep, which is a 9.79% improvement compared with MobilenetV2, with only a small increase in Params (the number of parameters) and FLOPs (floating-point operations). Compared to models such as Swin-small, which currently performs SOTA, Params and FLOPs are reduced by nearly ten times, whereas recognition accuracy is only 0.64% lower. Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.

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“…These methods are prone to cause serious physical damage to animals [ 4 ]. Electronic identification, using methods like Radio Frequency Identification (RFID) tags, are widely used in the field of individual livestock identification [ 5 ]. However, this method is costly and prone to problems such as unreadability or fraud.…”

Section: Introductionmentioning

confidence: 99%

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

Song

Liu

Wang

et al. 2022

Animals

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Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.

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Pig Face Identification Based on Improved Alexnet Model

Cited by 15 publications

References 15 publications

Facial Recognition of Cattle Based on SK-ResNet

Facial Recognition of Cattle Based on SK-ResNet

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

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