首页|Pigeon cleaning behavior detection algorithm based on light-weight network

Pigeon cleaning behavior detection algorithm based on light-weight network

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  • NSTL
  • Elsevier
? 2022The behavior of pigeons in the dovecote reflects their environmental comfort and health indicators. In order to solve the problems of time-consuming, labor-consuming, and subjectivity of traditional manual experience, an improved YOLO V4 light-weight target detection algorithm was proposed for row detection of breeding pigeons. Employ SPP, FPN, and PANet networks to strengthen the features retrieved from GhostNet as the backbone. To ensure accuracy, Ghostnet-yolo V4 reduced the model's number of parameters and raised its size to 43 MB. The light-weight feature extraction network GhostNet outperformed MobileNet V1~V3 under the modified model. Faster RCNN, SSD, YOLO V4 and YOLO V3 compression rates were increased by 43.4 percent, 35.8 percent, 70.1 percent, and 69.1 percent, respectively. The improved algorithm has an accuracy of 97.06 percent and a recognition speed of 0.028 s per frame. The improved model can provide a theoretical foundation and technological reference for detecting breeding pigeon behavior in real-time in a dovecote.

GhostnetLight-weight networkPigeon cleaning behaviorTarget detectionYOLO v4

Guo J.、He G.、Deng H.、Fan W.、Xu L.、Cao L.、Feng D.、Lv J.、Liu S.、Gul Hassan S.、Li J.、Wu H.

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Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology Zhongkai University of Agriculture and Engineering

College of Mechanical and Electric Engineerings Shihezi University

National S&T Innovation Center for Modern Agricultural Industry (Guangzhou) (Short for GNASIC)

2022

10.1016/j.compag.2022.107032

Computers and Electronics in Agriculture

Computers and Electronics in Agriculture

EISCI
ISSN：0168-1699
年,卷(期)：2022.199
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