Research on improved YOLOv8n based potato seedling detection in UAV remote sensing images.
Front Plant Sci
; 15: 1387350, 2024.
Article
em En
| MEDLINE
| ID: mdl-38751836
ABSTRACT
Introduction:
Accurate detection of potato seedlings is crucial for obtaining information on potato seedlings and ultimately increasing potato yield. This study aims to enhance the detection of potato seedlings in drone-captured images through a novel lightweight model.Methods:
We established a dataset of drone-captured images of potato seedlings and proposed the VBGS-YOLOv8n model, an improved version of YOLOv8n. This model employs a lighter VanillaNet as the backbone network in-stead of the original YOLOv8n model. To address the small target features of potato seedlings, we introduced a weighted bidirectional feature pyramid network to replace the path aggregation network, reducing information loss between network layers, facilitating rapid multi-scale feature fusion, and enhancing detection performance. Additionally, we incorporated GSConv and Slim-neck designs at the Neck section to balance accuracy while reducing model complexity.Results:
The VBGS-YOLOv8n model, with 1,524,943 parameters and 4.2 billion FLOPs, achieves a precision of 97.1%, a mean average precision of 98.4%, and an inference time of 2.0ms. Comparative tests reveal that VBGS-YOLOv8n strikes a balance between detection accuracy, speed, and model efficiency compared to YOLOv8 and other mainstream networks. Specifically, compared to YOLOv8, the model parameters and FLOPs are reduced by 51.7% and 52.8% respectively, while precision and a mean average precision are improved by 1.4% and 0.8% respectively, and the inference time is reduced by 31.0%.Discussion:
Comparative tests with mainstream models, including YOLOv7, YOLOv5, RetinaNet, and QueryDet, demonstrate that VBGS-YOLOv8n outperforms these models in terms of detection accuracy, speed, and efficiency. The research highlights the effectiveness of VBGS-YOLOv8n in the efficient detection of potato seedlings in drone remote sensing images, providing a valuable reference for subsequent identification and deployment on mobile devices.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Front Plant Sci
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China