南京农业大学学报2024，Vol.47Issue(3)：583-591,9.DOI:10.7685/jnau.202308002
结合改进CBAM和MobileNetV2算法对小麦病斑粒分类
Combining improved CBAM and MobileNetV2 algorithms for classifying diseased wheat kernels
摘要
Abstract
[Objectives]Intelligent detection of diseased wheat kernels is important for the efficient,rapid,and accurate evaluation of wheat kernel quality.Existing deep neural network models for the classification of diseased wheat kernels have disadvantages such as large numbers of parameters and complexity of operations,which make it unsuitable for the deployment of the model on edge computing devices,thus affecting the efficiency of on-site classification of diseased wheat kernels.In this paper,a lightweight neural network algorithm for diseased wheat kernel classification was proposed.[Methods]In this study,the model was developed based on the lightweight network MobileNetV2 and added an improved CBAM(convolutional block attention module)attention mechanism.The improved model was fully integer quantized and deployed to mobile devices.Moreover,the proposed model was applied to classify four types of wheat kernels(fusarium-damaged kernels,common bunt of wheat kernels,broken kernels,and normal kernels).[Results]Compared to the previous MobileNetV2 network,the model combining the improved attention mechanism and MobileNetV2 network was improved,and the accuracy,precision,and recall rates for the model were improved by 3.15%,3%and 3%,respectively.The improved model after full integer quantization achieved 99%,94%,99%and 96%recognition accuracy for fusarium-damaged kernels,common bunt of wheat kernels,broken kernels,and normal kernels,respectively.In addition,the size of the model was 2.36 MB,and the single inference time of this model at the edge computing device was 96.95 ms.[Conclusions]The improved algorithm of this paper has increased the model accuracy,reduced the size of the model,and accelerated the model inference speed.This study can provide guidance for the de-redundancy of fusarium-damaged kernel classification models.关键词
小麦病斑粒/注意力机制/轻量级神经网络/全整型量化Key words
diseased wheat kernels/attention mechanism/lightweight neural networks/full integer quantization分类
信息技术与安全科学引用本文复制引用
任治洲,梁琨,王泽宇,张群,郭雅欣,郭嘉琦..结合改进CBAM和MobileNetV2算法对小麦病斑粒分类[J].南京农业大学学报,2024,47(3):583-591,9.基金项目
江苏省自然科学基金项目(BK20221518) （BK20221518）
江苏省农业科技自主创新资金项目[CX(23)1002] （23）
