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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1590-1594

Cattle counting estimation method based on multi-scale residual visual information fusion

Yang Wu
Wang Yinghui
Tan Yao
Feng Xin
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0421

Abstract

Due to the uneven distribution and large scale-variation range of living cattle, the accuracy of traditional object counting algorithm is not high in living-animal field, and there are few cattle data sets used for research. To solve these problems, this paper established a dataset for cattle density estimation, and proposed a cattle counting estimation method based on multi-scale residual visual information fusion. This method used multiple parallel dilate convolution with different dilate rates to extract multi-scale features of cattle, and combined residual structure with small dilate rate convolution to design a deep neural network more suitable for living cattle object counting, thus eased the effect of grid effect caused by dilate convolution and better adapt to the multi-scale changes of cattle. The proposed method achieved the lowest mean absolute error(MAE) and root mean square error(RMSE) on the cattle density data set. In dense counting data sets, the MAE and the RMSE of the proposed method also achieved the optimal or suboptimal results. The experimental results show that the proposed method is not only suitable for the number estimation of cattle scene, but also has high accuracy and strong robustness in population density estimation.

Foundation Support

重庆市基础研究与前沿探索项目(重庆市自然科学基金)(cstc2018jcyjAX0287)
重庆理工大学教育创新计划资助项目(clgycx20202092)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0421
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Technology of Graphic & Image
Pages: 1590-1594
Serial Number: 1001-3695(2022)05-053-1590-05

Publish History

[2021-12-07] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

杨武, 王颖慧, 谈耀, 等. 基于多尺度残差视觉信息融合的牧场牛只数量估计方法 [J]. 计算机应用研究, 2022, 39 (5): 1590-1594. (Yang Wu, Wang Yinghui, Tan Yao, et al. Cattle counting estimation method based on multi-scale residual visual information fusion [J]. Application Research of Computers, 2022, 39 (5): 1590-1594. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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