A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network

Ren, Yufei; Huang, Yikang; Wang, Yichen; Zhang, Shijie; Qu, Hao; Ma, Jie; Wang, Longhe; Li, Lin

doi:10.3390/ani12182425

Cited by 8 publications

(8 citation statements)

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“…The chicken face is an essential feature in identifying chickens. To recognize the chickens’ age, we already performed day-age recognition on chicken face data [ 3 ]. However, in a natural chicken farm environment, as in Figure 11 and Figure 12 , we cannot obtain chicken face images directly.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Researchers used a Canon 5D digital camera to capture these images on 20 March 2022. Figure 1 and Table 2 [ 3 ] display details of this dataset, and each image’s resolution is

.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The growth stages of a chicken can be classified by its day-old age [ 3 ]; Table 1 exhibits the details.…”

Section: Introductionmentioning

confidence: 99%

“…Although these years have witnessed the boom of computer vision implementations in livestock detection, the applications in the field of this paper’s research are still limited—the computer-vision-based detection and artificial intelligence management for chickens are rarely seen, and concerning technologies and research data are comparatively not up to date, though chickens are one of the most common and traditional poultry. Among few pieces of research about the classification of the growth stages of chicks, one outstanding achievement is that Yufei Ren et al [ 3 ] presented an attention encoder to find chicken face features. They implemented this structure in different mainstream CNN (convolution neural networks) to search for the most excellent network for this task.…”

Section: Introductionmentioning

confidence: 99%

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An Advanced Chicken Face Detection Network Based on GAN and MAE

Huang

et al. 2022

Animals

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Achieving high-accuracy chicken face detection is a significant breakthrough for smart poultry agriculture in large-scale farming and precision management. However, the current dataset of chicken faces based on accurate data is scarce, detection models possess low accuracy and slow speed, and the related detection algorithm is ineffective for small object detection. To tackle these problems, an object detection network based on GAN-MAE (generative adversarial network-masked autoencoders) data augmentation is proposed in this paper for detecting chickens of different ages. First, the images were generated using GAN and MAE to augment the dataset. Afterward, CSPDarknet53 was used as the backbone network to enhance the receptive field in the object detection network to detect different sizes of objects in the same image. The 128×128 feature map output was added to three feature map outputs of this paper, thus changing the feature map output of eightfold downsampling to fourfold downsampling, which provided smaller object features for subsequent feature fusion. Secondly, the feature fusion module was improved based on the idea of dense connection. Then the module achieved feature reuse so that the YOLO head classifier could combine features from different levels of feature layers to capture greater classification and detection results. Ultimately, the comparison experiments’ outcomes showed that the mAP (mean average Precision) of the suggested method was up to 0.84, which was 29.2% higher than other networks’, and the detection speed was the same, up to 37 frames per second. Better detection accuracy can be obtained while meeting the actual scenario detection requirements. Additionally, an end-to-end web system was designed to apply the algorithm to practical applications.

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Section: Results and Discussionmentioning

confidence: 99%

“…Researchers used a Canon 5D digital camera to capture these images on 20 March 2022. Figure 1 and Table 2 [ 3 ] display details of this dataset, and each image’s resolution is

.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The growth stages of a chicken can be classified by its day-old age [ 3 ]; Table 1 exhibits the details.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Advanced Chicken Face Detection Network Based on GAN and MAE

Huang

et al. 2022

Animals

Self Cite

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“…The day-age of a chicken is a concept similar to human age [ 2 ]. It is an important indicator of its growth.…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing

Huang

Yang

Guo

et al. 2022

Animals

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Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK.

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