农业机械学报2024，Vol.55Issue(5)：186-195,10.DOI:10.6041/j.issn.1000-1298.2024.05.017
基于改进DeepLabV3+的荞麦苗期无人机遥感图像分割识别方法研究
Segmentation of Buckwheat by UAV Based on Improved Lightweight DeepLabV3+at Seedling Stage
摘要
Abstract
In view of the problems of high computational complexity,large memory consumption,and difficulty in deployment on mobile platforms with limited computing power in DeepLabV3+segmentation model,an improved lightweight DeepLabV3+algorithm was proposed to realize the segmentation and recognition of buckwheat by UAV at seedling stage.The algorithm adopted the fusion of re-parameterization visual geometry group(RepVGG)and mobile vision transformer(MobileViT)modules to establish the backbone network for feature extraction.At the same time,the squeeze-and-excitation networks(SENet)attention mechanism was introduced into the RepVGG network structure to capture more global semantic information by using the correlation between channels,and ensure the performance of buckwheat segmentation.Experimental results showed that compared with fully convolutional networks(FCN),pyramid scene parsing network(PSPNet),dense atrous spatial pyramid pooling(DenseASPP),DeepLabV3,and DeepLabV3+models,the improved algorithm proposed greatly reduced the model parameters,making it more suitable for deployment on mobile terminals.The mean pixel accuracy(mPA)and mean intersection over union(mIoU)on the self-built buckwheat segmentation dataset were 97.02％and 91.45％,the overall parameters,floating-point operations(FLOPs)and inference speed were 9.01 × 106,8.215 × 1010 and 37.83 f/s,respectively,with the best performance.In the full-size image segmentation,the mPA and mIoU for buckwheat segmentation can meet the requirements at different flight heights,which had good segmentation ability and inference speed.The algorithm can provide technical support for the later buckwheat seed replacement,fertilization maintenance,and growth monitoring,and promote the intelligent development of small and coarse grain industry.关键词
荞麦苗期/无人机遥感/图像语义分割/DeepLabV3+/轻量化Key words
buckwheat at seedling stage/UAV remote sensing/image segmentation/DeepLabV3+/lightweight分类
信息技术与安全科学引用本文复制引用
武锦龙,吴虹麒,李浩,雷兴鹏,宋海燕..基于改进DeepLabV3+的荞麦苗期无人机遥感图像分割识别方法研究[J].农业机械学报,2024,55(5):186-195,10.基金项目
国家重点研发计划项目(2021YFD1600602-09)和山西省基础研究计划项目(202203021212414、202303021222067) （2021YFD1600602-09）
