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SSD-YOLO:用于检测叶病的轻量级网络

Canlin Pan¹, Shen Wang^1,2, Yahui Wang³

¹School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.

Frontiers in plant science

|September 3, 2025

概括

这项研究介绍了SSD-YOLO,一种用于检测大米叶病的增强型YOLOv8模型. 它显著提高了在识别大米棕色斑点,大米爆发和细菌爆发方面的准确性和效率.

科学领域:

农业科学
计算机视觉
机器学习

背景情况:

叶病严重影响作物产量和质量.
传统的诊断方法是主观的,容易出错.
准确有效的疾病检测对于米种植至关重要.

研究的目的:

开发一种基于YOLOv8的改进方法来准确检测大米疾病.
提高复杂疾病模式的特征提取和采样精度.
在具有挑战性的环境条件下提高模型性能.

主要方法:

实施了一种新的SSD-YOLO模型,整合了SENet的注意力.
使用动态样本 (DySample) 模块来提高提升样本的准确性.
用于增强检测的形状感知交叉点 (ShapeIoU) 损失.
在3000张大米叶病图像的数据集上训练和验证模型.

主要成果:

SSD-YOLO的检测准确度很高:棕色斑点为87.52%,爆裂为99.48%,细菌爆裂为98.99%.
与原始YOLOv8相比,观察到显著的改善:棕色斑点为11. 11%,爆发为1. 73%,细菌爆发为3. 81%.
该模型是紧的 (6MB),同时显示了增强的检测准确性和速度.

结论:

SSD-YOLO模型为及时识别病提供了强大而高效的解决方案.
整合SENet,Dysample和ShapeIoU损失显著提高了检测性能.
这种方法为精准农业和养病管理提供了宝贵的支持.

关键词:

这里是YOLO.注意力机制深度学习疾病鉴定对象检测

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