摘要
为解决黄瓜病害种类繁多且症状相似导致的识别困难问题,本文提出一种改进的YOLOv9黄瓜病害识别模型BiFEL-YOLOv9,以提高自然背景下黄瓜病害的检测精度.首先在关键网络层引入加权双向特征金字塔网络模块(bidirectional feature pyramid network, BiFPN),增强了模型对多尺度特征的融合能力;其次结合特征增强模块(feature enhancement)和大核选择性注意力机制(large selective kernel block, LSKBlock)对原始的RepNCSPELAN4模块进行改进得到RNFEL模块,增强了模型的特征表示能力及对复杂背景的鲁棒性.实验结果表明, BiFELYOLOv9模型准确率达到97.96%、召回率达到95.51%、平均精度均值mAP_0.5和mAP_0.5:0.95分别达到98.21%和95.12%,均优于原YOLOv9模型,有效实现了黄瓜病害的检测与识别.
To address the difficulty in identifying cucumber diseases caused by numerous varieties and similar symptoms,this study proposes an improved YOLOv9 model for cucumber disease recognition,named BiFEL-YOLOv9,to enhance detection accuracy in natural backgrounds.Initially,a weighted bidirectional feature pyramid network module is incorporated into critical network layers to enhance the model’s multi-scale feature fusion capability.Following that,the original RepNCSPELAN4 module is enhanced by integrating a feature enhancement module and a large selective kernel block(LSKBlock)to obtain the RNFEL module,which improves the model’s feature representation capability and robustness to complex backgrounds.Experimental results indicate that the BiFEL-YOLOv9 model achieves an accuracy of 97.96%,a recall rate of 95.51%,and mean average precision scores of 98.21%for mAP_0.5 and 95.12%for mAP_0.5:0.95,all of which surpass the performance of the original YOLOv9 model.The proposed model effectively accomplishes the detection and recognition of cucumber diseases.
作者
邵佳慧
姚百蔚
田宏
SHAO Jia-Hui;YAO Bai-Wei;TIAN Hong(College of Rail Intelligent Engineering,Dalian Jiaotong University,Dalian 116052,China)
出处
《计算机系统应用》
2025年第7期208-214,共7页
Computer Systems & Applications
基金
国家自然科学基金重点项目(32130085)。
