Research on Behavior Recognition of Dairy Goat Based on Multi-model Fusion

Li, Yi; Tang, Jing; Dong, Haiqing

doi:10.1145/3449388.3449395

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, other studies on livestock behavior detection only had a maximum of 27 dairy cows per image. When the number of livestock is small, the accuracy of the references [31][32][33][34][35][36] is lower than that of the CEBD-YOLO model, and as the number of livestock increases, the accuracy of these methods will decrease. The object of this study was a natural breeding environment containing 200 cows.…”

Section: Discussionmentioning

confidence: 99%

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

Wang

Gao

et al. 2022

Agriculture

View full text Add to dashboard Cite

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in ideal environments with a small number of cows and has a low inference speed and accuracy in natural scenes. To improve the inference speed and accuracy of cow estrus behavior in natural scenes, this paper proposes a cow estrus behavior detection method based on the improved YOLOv5. By improving the YOLOv5 model, it has stronger detection ability for complex environments and multi-scale objects. First, the atrous spatial pyramid pooling (ASPP) module is employed to optimize the YOLOv5l network at multiple scales, which improves the model’s receptive field and ability to perceive global contextual multiscale information. Second, a cow estrus behavior detection model is constructed by combining the channel-attention mechanism and a deep-asymmetric-bottleneck module. Last, K-means clustering is performed to obtain new anchors and complete intersection over union (CIoU) is used to introduce the relative ratio between the predicted box of the cow mounting and the true box of the cow mounting to the regression box prediction function to improve the scale invariance of the model. Multiple cameras were installed in a natural breeding scene containing 200 cows to capture videos of cows mounting. A total of 2668 images were obtained from 115 videos of cow mounting events from the training set, and 675 images were obtained from 29 videos of cow mounting events from the test set. The training set is augmented by the mosaic method to increase the diversity of the dataset. The experimental results show that the average accuracy of the improved model was 94.3%, that the precision was 97.0%, and that the recall was 89.5%, which were higher than those of mainstream models such as YOLOv5, YOLOv3, and Faster R-CNN. The results of the ablation experiments show that ASPP, new anchors, C3SAB, and C3DAB designed in this study can improve the accuracy of the model by 5.9%. Furthermore, when the ASPP dilated convolution was set to (1,5,9,13) and the loss function was set to CIoU, the model had the highest accuracy. The class activation map function was utilized to visualize the model’s feature extraction results and to explain the model’s region of interest for cow images in natural scenes, which demonstrates the effectiveness of the model. Therefore, the model proposed in this study can improve the accuracy of the model for detecting cow estrus events. Additionally, the model’s inference speed was 71 frames per second (fps), which meets the requirements of fast and accurate detection of cow estrus events in natural scenes and all-weather conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

Wang

Gao

et al. 2022

Agriculture

View full text Add to dashboard Cite

show abstract

A lightweight cow mounting behavior recognition system based on improved YOLOv5s

Wang,

Gao,

et al. 2023

Sci Rep

View full text Add to dashboard Cite

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms.

show abstract

Research on Behavior Recognition of Dairy Goat Based on Multi-model Fusion

Cited by 2 publications

References 8 publications

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

A lightweight cow mounting behavior recognition system based on improved YOLOv5s

Contact Info

Product

Resources

About