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LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8

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成果类型:
期刊论文
作者:
Feng, Haoran;Chen, Xiqu;Duan, Zhaoyan
通讯作者:
Chen, XQ
作者机构:
[Duan, Zhaoyan; Chen, Xiqu; Feng, Haoran] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
通讯机构:
[Chen, XQ ] W
Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
deep learning;cotton pests and diseases;lightweight model;C2f
期刊:
Agriculture-Basel
ISSN:
2077-0472
年:
2025
卷:
15
期:
4
基金类别:
Science and Technology Program of Hubei Provincial Department of Education; [B2020067]
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
摘要:
To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing de...

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