Quality Detection Method of Penaeus vannamei Based on Lightweight YOLOv5s Network

Yanyi Chen, Xuhong Huang (), Cunxin Zhu, Shengping Tang, Nan Zhao and Weihao Xiao
Additional contact information
Yanyi Chen: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Xuhong Huang: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Cunxin Zhu: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Shengping Tang: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Nan Zhao: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Weihao Xiao: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China

Agriculture, 2023, vol. 13, issue 3, 1-15

Abstract: The appearance and meat quality of Penaeus vannamei are important indexes in the production process, and the quality of the product will be reduced if the defective shrimp is mixed in during processing. In order to solve this problem, a quality detection model of Penaeus vannamei based on deep learning was put forward. Firstly, the self-made dataset of Penaeus vannamei was expanded to enhance the generalization ability of the neural network. Secondly, the backbone of YOLOv5 (you only look once v5) is replaced by the lightweight network PP-LCNet that removes the dense layer at the end, which reduces the model parameters and calculation. Then, the 7 × 7 convolution DepthSepConv module is embedded in a PP-LCNet backbone, which effectively strengthens the feature extraction ability of the network. Ultimately, SiLU activation function is used to replace the Hardsigmoid and Hardswish activation functions in the PP-LCNet backbone to enhance the regularization ability and detection speed of the network. Through comparative experiments, the all-round performance of the Shrimp-YOLOv5s network is higher than the current mainstream classical model and the lightweight model. The mAP@0.5, mAP@0.5:0.95, detection speed, parameters, and calculation of Shrimp-YOLOv5s are 98.5%, 88.1%, 272.8 FPS (frames per second), 4.8 M, and 9.0 GFLOPs (giga floating point operations) respectively.

Keywords: Penaeus vannamei; YOLOv5; lightweight; deep learning (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: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/3/690/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/3/690/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:3:p:690-:d:1099213

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().