计算机科学与探索2024，Vol.18Issue(8)：2118-2129,12.DOI:10.3778/j.issn.1673-9418.2307022
Dynamic-YOLOX:复杂背景下的苹果叶片病害检测模型
Dynamic-YOLOX:Detection Model for Apple Leaf Disease in Complex Background
摘要
Abstract
To address the issues of incomplete disease types and the single background of apple leaf images in the apple leaf disease dataset,this paper constructs a new dataset comprising six common apple leaf diseases with com-plex backgrounds.Additionally,this paper designs Dynamic-YOLOX based on YOLOX-S(you only look once X-S)for the detection of apple leaf disease,aiming to solve the problems of low accuracy,complex models,and insuffi-cient real-time monitoring.Firstly,the ECA-SPPFCSPC(efficient channel attention cross-stage partial fast spatial pyramid pooling module)is devised and employed to replace the SPP(spatial pyramid pooling)and CSPNet(cross-stage partial network)components in the Dark5 segment of the YOLOX-S model backbone,aiming to reinforce the model ability to focus on deep semantic features,suppress irrelevant information and reduce hardware memory over-head.Secondly,the ODCSP(omni-dimensional dynamic cross-stage partial network)module is designed to replace all the CSPNet of the Dark2,Dark3,Dark4 segments in the YOLOX-S model backbone and neck network.This de-sign enhances the model adaptability to various input features,reducing parameter and computational overhead while improving the average detection accuracy of the model.Finally,the Varifocal Loss is introduced to replace the BCEWithLogits Loss for classification confidence loss in the model to elevate the detection accuracy of dense small target diseases in apple leaves.On the homemade dataset,Dynamic-YOLOX demonstrates a relative mAP improve-ment of 4.54 percentage points over the original YOLOX-S model,achieving 84.63%.Simultaneously,the Params and FLOPs of the model decreases by 11.97%and 13.45%,respectively,and the detection speed reaches 44.07 FPS.Dynamic-YOLOX also exhibits a certain degree of superiority compared with mainstream apple leaf disease detec-tion models.关键词
苹果叶部病害/目标检测/YOLOX/动态跨阶段局部网络(ODCSP)/Varifocal LossKey words
apple leaf diseases/object detection/YOLOX/omni-dimensional dynamic cross-stage partial network(ODCSP)/Varifocal Loss分类
信息技术与安全科学引用本文复制引用
盛帅,段先华,胡维康,曹伟杰..Dynamic-YOLOX:复杂背景下的苹果叶片病害检测模型[J].计算机科学与探索,2024,18(8):2118-2129,12.基金项目
国家自然科学基金(62276118). This work was supported by the National Natural Science Foundation of China(62276118). （62276118）
