Goat-Face Recognition in Natural Environments Using the Improved YOLOv4 Algorithm

Fu Zhang, Shunqing Wang, Xiahua Cui, Xinyue Wang, Weihua Cao, Huang Yu, Sanling Fu () and Xiaoqing Pan ()
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Fu Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Shunqing Wang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Xiahua Cui: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Xinyue Wang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Weihua Cao: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Huang Yu: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
Sanling Fu: College of Physical Engineering, Henan University of Science and Technology, Luoyang 471023, China
Xiaoqing Pan: Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China

Agriculture, 2022, vol. 12, issue 10, 1-14

Abstract: In view of the low accuracy and slow speed of goat-face recognition in real breeding environments, dairy goats were taken as the research objects, and video frames were used as the data sources. An improved YOLOv4 goat-face-recognition model was proposed to improve the detection accuracy; the original backbone network was replaced by a lightweight GhostNet feature extraction network. The pyramid network of the model was improved to a channel management mechanism with a spatial pyramid structure. The path aggregation network of the model was improved into a fusion network with residual structure in the form of double parameters, in order to improve the model’s ability to detect fine-grained features and distinguish differences between similar faces. The transfer learning pre-training weight loading method was adopted, and the detection speed, the model weight, and the mean average precision (mAP) were used as the main evaluation indicators of the network model. A total of 2522 images from 30 dairy goats were augmented, and the training set, validation set, and test set were divided according to 7:1:2. The test results of the improved YOLOv4 model showed that the mAP reached 96.7%, and the average frame rate reached 28 frames/s in the frontal face detection. Compared with the traditional YOLOv4, the mAP improved by 2.1%, and the average frame rate improved by 2 frames/s. The new model can effectively extract the facial features of dairy goats, which improves the detection accuracy and speed. In terms of profile face detection, the average detection accuracy of the improved YOLOv4 goat-face-recognition network can reach 78%. Compared with the traditional YOLOv4 model, the mAP increased by 7%, which effectively demonstrated the improved profile recognition accuracy of the model. In addition, the improved model is conducive to improving the recognition accuracy of the facial poses of goats from different angles, and provides a technical basis and reference for establishing a goat-face-recognition model in complex situations.

Keywords: channel management mechanism; YOLOv4; fused residuals; transfer learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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