Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN

Liangben Cao, Zihan Xiao, Xianghui Liao, Yuanzhou Yao, Kangjie Wu, Jiong Mu, Jun Li and Haibo Pu
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Liangben Cao: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Zihan Xiao: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Xianghui Liao: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Yuanzhou Yao: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Kangjie Wu: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jiong Mu: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jun Li: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Haibo Pu: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China

Agriculture, 2021, vol. 11, issue 6, 1-15

Abstract: The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to calculate the density of chickens quickly and accurately because of the complicated environmental background and the dynamic number of chickens. Therefore, we propose an automated method for quickly and accurately counting the number of chickens on a chicken farm, rather than doing so manually. The contributions of this paper are twofold: (1) we innovatively designed a full convolutional network—DenseFCN—and counted the chickens in an image using the method of point supervision, which achieved an accuracy of 93.84% and 9.27 frames per second (FPS); (2) the point supervision method was used to detect the density of chickens. Compared with the current mainstream object detection method, the higher effectiveness of this method was proven. From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding.

Keywords: deep learning; aquaculture automation; computer vision; chicken detection (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: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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