Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1628002(2021)

Individual Cow Recognition Based on Convolution Neural Network and Transfer Learning

Yongxin Xing, Biqiao Wu, Songping Wu, and Tianyi Wang*
Author Affiliations
  • College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, Guizhou, China
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    To realize accurate identification of individual cows in a complex farm environment, the SSD (single shot multibox detector) algorithm was improved to solve the problem of poor detection effect for overlapping objects. First, different feature maps were fused to ensure that different feature maps complemented each other and improved the detection effect of overlapping objects. Then, Conv4_3 was removed from the network. The number of candidate frames in other feature maps increased, ensuring the real-time performance of the algorithm and also improving the detection accuracy. Finally, the transfer learning method was used to improve the average accuracy of the algorithm. The experimental results show that compared with the traditional SSD algorithm, the average accuracy (AP) of the improved SSD algorithm is improved by 4.32% in real-time detection, and the AP of the improved SSD algorithm is increased by 3.85% after migration.

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    Yongxin Xing, Biqiao Wu, Songping Wu, Tianyi Wang. Individual Cow Recognition Based on Convolution Neural Network and Transfer Learning[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1628002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 30, 2020

    Accepted: Dec. 8, 2020

    Published Online: Aug. 20, 2021

    The Author Email: Wang Tianyi (tywang@gzu.edu.cn)

    DOI:10.3788/LOP202158.1628002

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