农业机械学报2024，Vol.55Issue(5)：254-262,272,10.DOI:10.6041/j.issn.1000-1298.2024.05.024
基于选择性注意力神经网络的木薯叶病害检测算法
Cassava Leaf Disease Detection Algorithm Based on Selective Attention Neural Network
摘要
Abstract
To achieve high-precision detection of four major cassava leaf diseases in complex unstructured environments,an improved algorithm for cassava leaf disease neural network detection based on the selective attention mechanism,MAISNet,was proposed.Using V2-ResNet-101 as the base network,the multiattention algorithm was firstly used to optimize the weighting coefficients,adjust the semantic expression of the feature channels,and the semantic feature saliency expression of cassava leaf disease in the feature map was preliminary constructed;then the instance batch normalization method was used after the residual unit to suppress the covariate offset in the feature expression,highlight the target semantic feature expression in the feature map,and realize the high-quality semantic feature expression.Finally,the Squareplus activation function was used to replace the ReLU activation function in the residual branch to maintain the numerical distribution of semantic features in the negative domain,and reduce the truncation errors in the feature fitting process.The results of the comparison test showed that the MAISNet-101 neural network constructed after the above improvement achieved an average accuracy of 95.39％for the detection of four common cassava leaf diseases,which was significantly better than the performance of the mainstream algorithms such as EfficientNet-B5 and RepVGG-B3g4.The results of the visualization and analysis of the extracted features of the network showed that high-quality semantic feature saliency representation of cassava leaf diseases was the key to improve the accuracy of cassava leaf disease detection.The proposed MAISNet neural network model can accomplish high-precision detection of cassava leaf diseases in real scenarios,which can provide technical support for precise drug application.关键词
木薯/病害检测/多重注意力算法/显著性语义特征/Squareplus激活函数Key words
cassava/disease detection/multiattention algorithm/saliency semantic feature/Squareplus activation function分类
信息技术与安全科学引用本文复制引用
张家瑜,朱锐,邱威,陈坤杰..基于选择性注意力神经网络的木薯叶病害检测算法[J].农业机械学报,2024,55(5):254-262,272,10.基金项目
江苏省农业科技自主创新资金项目(CX(20)3172) （CX(20）
