Paper
3 February 2023 A method for improving YOLOv5s-based plant leaf cells recognition rate using transfer learning
Gaixing Wei, Wenlong Yi, Yingding Zhao, Yucheng Liu, Yilu Xu, Hua Yin
Author Affiliations +
Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251137 (2023) https://doi.org/10.1117/12.2660036
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China
Abstract
In view of the small sample set of plant cell images, unclear cell boundaries and artifacts, the deep learning model has a low recognition rate. In this study, a yolov5s parameter transfer method is used to improve the recognition accuracy of plant cells. First, perform data enhancement operations such as denoising, rotation, translation and scaling on the plant cell training samples; then, pre-train the yolov5s network on the BCCD Dataset, transfer the model parameters to the network, and then carry out model parameter update training on the plant cell data set. The experimental results showed that the transfer learning method was faster than the original yolov5s network on the model training and validation set in terms of algorithm convergence, with a smaller loss function value. This method can provide a precise cell positioning solution to measure the plant cell geometric parameters.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gaixing Wei, Wenlong Yi, Yingding Zhao, Yucheng Liu, Yilu Xu, and Hua Yin "A method for improving YOLOv5s-based plant leaf cells recognition rate using transfer learning", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251137 (3 February 2023); https://doi.org/10.1117/12.2660036
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KEYWORDS
Data modeling

Detection and tracking algorithms

Statistical modeling

Neural networks

Imaging systems

Visualization

Image classification

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