CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement
With the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely...
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MDPI AG
2025-02-01
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| author | Guohong Gao Yuxin Ma Jianping Wang Zhiyu Li Yan Wang Haofan Bai |
| author_facet | Guohong Gao Yuxin Ma Jianping Wang Zhiyu Li Yan Wang Haofan Bai |
| author_sort | Guohong Gao |
| collection | DOAJ |
| description | With the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. In this paper, we propose a novel cow face detection network based on YOLOv7 improvement, named CFR-YOLO. First of all, the method of extracting the features of a cow’s face (including nose, eye corner, and mouth corner) is constructed. Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. The RFB module is introduced in the backbone network; and in the head layer, the CBAM convolutional attention module is introduced. The performance of CFR-YOLO is compared with other mainstream deep learning models (including YOLOv7, YOLOv5, YOLOv4, and SSD) on a self-built cow face dataset. Experiments indicate that the CFR-YOLO model achieves 98.46% accuracy (precision), 97.21% recall (recall), and 96.27% average accuracy (mAP), proving its excellent performance in the field of cow face detection. In addition, comparative analyses with the other four methods show that CFR-YOLO exhibits faster convergence speed while ensuring the same detection accuracy; and its detection accuracy is higher under the condition of the same model convergence speed. These results will be helpful to further develop the cattle identification technique. |
| format | Article |
| id | doaj-art-5deb7823edae44f5b93bfbd02345dd15 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5deb7823edae44f5b93bfbd02345dd152025-08-20T03:12:12ZengMDPI AGSensors1424-82202025-02-01254108410.3390/s25041084CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 ImprovementGuohong Gao0Yuxin Ma1Jianping Wang2Zhiyu Li3Yan Wang4Haofan Bai5School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaWith the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. In this paper, we propose a novel cow face detection network based on YOLOv7 improvement, named CFR-YOLO. First of all, the method of extracting the features of a cow’s face (including nose, eye corner, and mouth corner) is constructed. Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. The RFB module is introduced in the backbone network; and in the head layer, the CBAM convolutional attention module is introduced. The performance of CFR-YOLO is compared with other mainstream deep learning models (including YOLOv7, YOLOv5, YOLOv4, and SSD) on a self-built cow face dataset. Experiments indicate that the CFR-YOLO model achieves 98.46% accuracy (precision), 97.21% recall (recall), and 96.27% average accuracy (mAP), proving its excellent performance in the field of cow face detection. In addition, comparative analyses with the other four methods show that CFR-YOLO exhibits faster convergence speed while ensuring the same detection accuracy; and its detection accuracy is higher under the condition of the same model convergence speed. These results will be helpful to further develop the cattle identification technique.https://www.mdpi.com/1424-8220/25/4/1084cow face detectionYOLOv7deep learningtarget detection |
| spellingShingle | Guohong Gao Yuxin Ma Jianping Wang Zhiyu Li Yan Wang Haofan Bai CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement Sensors cow face detection YOLOv7 deep learning target detection |
| title | CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement |
| title_full | CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement |
| title_fullStr | CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement |
| title_full_unstemmed | CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement |
| title_short | CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement |
| title_sort | cfr yolo a novel cow face detection network based on yolov7 improvement |
| topic | cow face detection YOLOv7 deep learning target detection |
| url | https://www.mdpi.com/1424-8220/25/4/1084 |
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