Research on sheep face recognition algorithm based on improved AlexNet model

Zhang, Cheng; Zhang, Hao; Fang, Tong; Zhou, Yong; Song, Zhao; Du, Xiaoyong

doi:10.1007/s00521-023-08413-3

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…On a self-built sheep face dataset, SheepFaceNet can recognize 387 sheep face images per second with a recognition accuracy of 97.75%, achieving the best speed–accuracy trade-off. Compared to [ 5 , 6 , 7 , 8 , 9 , 10 ], SheepFaceNet has a similar recognition accuracy, but its model parameter size, computational complexity, and inference time delay are much lower than those of traditional models. While existing heavyweight backbone networks can achieve good recognition accuracy, their limitations in parameter size, computational complexity, and inference time delay prevent them from being deployed on edge devices.…”

Section: Discussionmentioning

confidence: 99%

“…Sheep face recognition extracts features through a convolutional neural network, represents the sheep face as a vector, compares it with the sheep faces stored in a database, finds the one with the highest similarity, and gives the identity information of the sheep. For example, [ 5 , 6 , 7 , 8 , 9 , 10 ] improved the efficiency and effectiveness of sheep face recognition using neural-network-based methods with high recognition accuracy. However, they use heavyweight neural networks for sheep face detection and recognition, leading to a large number of parameters in the recognition model, complex computations, slow inference speed, and impossible deployment on resource-limited edge devices.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2023

Animals

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The recognition of sheep faces based on computer vision has improved the efficiency and effectiveness of individual sheep identification, providing technical support for the development of smart farming. However, current recognition models have problems such as large parameter sizes, slow recognition speed, and difficult deployment. Therefore, this paper proposes an efficient and fast basic module called Eblock and uses it to build a lightweight sheep face recognition model called SheepFaceNet, which achieves the best balance between speed and accuracy. SheepFaceNet includes two modules: SheepFaceNetDet for detection and SheepFaceNetRec for recognition. SheepFaceNetDet uses Eblock to construct the backbone network to enhance feature extraction capability and efficiency, designs a bidirectional FPN layer (BiFPN) to enhance geometric location ability, and optimizes the network structure, which affects inference speed, to achieve fast and accurate sheep face detection. SheepFaceNetRec uses Eblock to construct the feature extraction network, uses ECA channel attention to improve the effectiveness of feature extraction, and uses multi-scale feature fusion to achieve fast and accurate sheep face recognition. On our self-built sheep face dataset, SheepFaceNet recognized 387 sheep face images per second with an accuracy rate of 97.75%, achieving an advanced balance between speed and accuracy. This research is expected to further promote the application of deep-learning-based sheep face recognition methods in production.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

2023

Animals

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“…For livestock, facial recognition for pigs and cows has become relatively mature. However, there is less application of facial recognition for sheep, especially using lightweight neural networks [22][23][24]. Additionally, there is a lack of large publicly available sheep facial recognition datasets.…”

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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“…With the progress of information technology such as artificial intelligence and deep learning, the identification technology of production objects, diseases, and behaviors in the agricultural field has been continuously developed and has been widely used in different fields of the industry [13,14]. However, compared with static objects such as rice and plants [15,16], and large land-based animals such as cattle and sheep [17,18], the development of underwater freestyle moving-target recognition technology is slow, and relevant studies have mostly focused on application scenarios where specific working conditions and training data are easy to obtain [19]. To solve this problem, transfer learning technology has been introduced into the field of fish identification.…”

Section: Introductionmentioning

confidence: 99%

Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning

Liu

Qian

et al. 2023

JMSE

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Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. To solve these problems, we proposed a new method for identifying large yellow croaker based on the CycleGAN (cycle generative adversarial network) and transfer learning. This method constructs source sample sets and target sample sets by acquiring large yellow croaker images in controllable scenes and actual farming conditions, respectively. The CycleGAN was used as the basic framework for image transformation from the source domain to the target domain to realize data amplification in the target domain. In particular, IDF (identity foreground loss) was used to optimize identity loss judgment criteria, and MMD (maximum mean discrepancy) was used to narrow the distribution between the source domain and target domain. Finally, transfer learning was carried out with the expanded samples to realize the identification of large yellow croaker under varying conditions. The experimental results showed that the proposed method achieved good identification results in both the controlled scene and the actual culture scene, with an average recognition accuracy of 96.9% and 94%, respectively. These provide effective technical support for the next steps in fish behavior tracking and phenotype measurement.

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Research on sheep face recognition algorithm based on improved AlexNet model

Cited by 7 publications

References 14 publications

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

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

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

Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning

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