信阳师范学院学报（自然科学版）2024，Vol.37Issue(2)：246-251,6.DOI:10.3969/j.issn.1003-0972.2024.02.018
一种茶叶病害的深度学习检测算法
A Deep Learning Detection Algorithm for Tea Diseases
摘要
Abstract
An improved deep learning algorithm based tea disease target detection was proposed.A coordinate attention mechanism was incorporated into the network,which could enable the model to refine features and focus more on disease information,thereby suppress the interference from background factors such as branches and weeds.CIoU was selected as the loss function of the model to improve the localization capabilities.Simultaneously,the target bounding boxes in the dataset was optimized through clustering techniques to obtain more accurate prior boxes.To address the issue of insufficient disease image data,a tea diseases dataset comprising six disease types was established.Experimental results showed that,compared to other algorithms,the presented method could exhibit superior performance across multiple metrics,and provide an efficient solution for the intelligent diagnosis of tea diseases.关键词
茶叶/病害检测/深度学习/目标检测/注意力机制Key words
tea/disease detection/deep learning/object detection/attention mechanisms分类
信息技术与安全科学引用本文复制引用
孙艳歌,吴飞,周棋赢..一种茶叶病害的深度学习检测算法[J].信阳师范学院学报（自然科学版）,2024,37(2):246-251,6.基金项目
国家自然科学基金项目(62062004) （62062004）
河南省自然科学基金项目(222300420274) （222300420274）
信阳师范学院研究生科研创新基金项目(2021KYJJ56) （2021KYJJ56）
