An Accurate and Lightweight Algorithm for Caged Chickens Detection based on Deep Learning

Yu Liu; Haibo Zhou; Zhengshuai Ni; Zhangjun Jiang; Xin Wang

doi:10.21162/PAKJAS/24.138

Author(s)

Yu Liu National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory for Advanced Me chatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Haibo Zhou National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory for Advanced Me chatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Zhengshuai Ni National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory for Advanced Me chatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Zhangjun Jiang National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory for Advanced Me chatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Xin Wang National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory for Advanced Me chatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Abstract

The animal husbandry industry is undergoing a transition towards intelligent breeding with advancements in artificial intelligence technology. Ensuring the health and safety of livestock and poultry requires automated assessment of their condition, with object detection being the primary focus. However, the existing object detection methods demonstrate subpar accuracy when applied to caged chicken detection. Moreover, their deployment on embedded devices is hindered by the significant size of the network models. To address these issues, an Improved-You Only Look Once version 5 (YOLOv5s) network detection model is proposed, which is based on YOLOv5s object detection algorithms. The cross-stage partial structure of the neck is modified to enhance the residual structure. Additionally, a convolutional block attention module is incorporated to extract crucial features. The distance intersection over union non-maximum suppression algorithm is employed to improve the identification of overlapping chickens. Furthermore, depthwise separable convolution is implemented to reduce network complexity. Experimental results on the custom dataset demonstrate that the Improved-YOLOv5s model achieves a mean average precision of 98.28%, surpassing the original model by 5.33%. It also exhibits strong performance across all other evaluation metrics while significantly reducing network parameters. In comparison to SSD, Faster-RCNN, YOLOv3, YOLOv4, YOLOv5s, and YOLOX-m, the Improved-YOLOv5s model demonstrates notable advantages in detection accuracy, network complexity, and detection speed.

Publication Details

Page(s) 403-415

DOI 10.21162/PAKJAS/24.138

Published Journal: Pakistan Journal of Agricultural Sciences, Volume: 61, Issue: 2, Year: 2024

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Keywords

Deep learning, caged chickens, image recognition, YOLOv5s, smart farming.