Detection and Localization of Albas Velvet Goats Based on YOLOv4
Author:
Guo Ying12, Wang Xihao3, Han Mingjuan3ORCID, Xin Jile3ORCID, Hou Yun3, Gong Zhuo3, Wang Liang3, Fan Daoerji3, Feng Lianjie3, Han Ding34
Affiliation:
1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China 2. College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China 3. College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China 4. Inner Mongolia State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Hohhot 010020, China
Abstract
In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.
Funder
National Key R&D Program of China Major Science and Technology Projects of the Inner Mongolia Autonomous Region Scientific Research Projects of Universities Directly of the Inner Mongolia Autonomous Region
Subject
General Veterinary,Animal Science and Zoology
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