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- W4378804562 abstract "The accurate identification of cow is the premise of individual filing, precise feeding, behavior monitoring and disease prevention. Due to the non-cooperative of cow, and the complexity of the actual pasture environment, some of the acquired images have data features that are obscured or unclear when collecting images of cows' faces. For this, the CD-YOLOv7 model has been proposed in this study, which is based on YOLOv7 network, and CBAM (Convolutional Block Attention Module) is integrated into it to improve the network's ability to extract effective features. In the neck part, the MPConv is improved by using DSConv (Depthwise Separable Convolution) to build the DS-MPConv, which improves the network detection speed and accuracy. The mAP of CD-YOLOv7 model is 98.55%, and the FPS is 31 f/s. Compared with Faster R-CNN, YOLOv3, YOLOv4, YOLOv5m and YOLOv7, mAP improved by 4.71%, 3.22%, 3.85%, 2.37% and 1.92% respectively. The experimental results show that the proposed model in this study can achieve the accurate identification of cow in the complex environment of pasture." @default.
- W4378804562 created "2023-06-01" @default.
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- W4378804562 date "2023-02-10" @default.
- W4378804562 modified "2023-10-18" @default.
- W4378804562 title "Study on individual identification method of cow based on CD-YOLOv7" @default.
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- W4378804562 doi "https://doi.org/10.1145/3592686.3592717" @default.
- W4378804562 hasPublicationYear "2023" @default.
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