华南农业大学学报2024，Vol.45Issue(2)：280-292,13.DOI:10.7671/j.issn.1001-411X.202304019
基于混合分组扩张卷积的玉米植株图像深度估计
Depth estimation for corn plant images based on hybrid group dilated convolution
摘要
Abstract
[Objective]To study the image depth estimation methods for corn field scenes,solve the problem of insufficient accuracy in depth estimation models due to the lack of effective photometric loss measures,and provide technical support for the vision system design of field intelligent agricultural machinery and navigation obstacle avoidance.[Method]This study applied binocular cameras as visual sensors,and proposed an unsupervised depth estimation model based on hybrid grouping extended convolution.A hybrid grouping extended convolution structure and its corresponding self-attention regulation mechanism were designed.The reverse residual module and deep neural network were constructed as the backbone of the model.The illumination insensitive image gradient and Gabor texture features were introduced into the apparent difference measurement of view,and the model optimization objective was constructed based on them.Taking maize plant image as an example,the model training and verification tests were carried out.[Result]Compared with the fixed expansion factor,the average relative error of maize plant depth estimation in the field was reduced by 63.9%,the average absolute error and root mean square error were reduced by 32.3%and 10.2%respectively,and the accuracy of the model was significantly improved.With the introduction of image gradient,Gabor texture feature and self-attention mechanism,the mean absolute error and root mean square error of field scene depth estimation were further reduced by 3.2%and 4.6%respectively.Increasing the network width and depth of shallow encoder could significantly improve the accuracy of model depth estimation,but the effect of this treatment on deep encoder was not obvious.The self-attention mechanism designed in this study was selective to the convolution grouping of different expansion factors in the shallow reverse residual module of the encoder,indicating that the mechanism had the ability to adjust the receptive field.Compared with Monodepth2,the average relative error and the average absolute error of the estimated depth of maize plants in the field of the research model were reduced by 48.2%and 17.1%respectively.Within the sampling range of 20 m,the average absolute error of the estimated depth was no more than 16 cm,and the calculation speed was 14.3 frames per second.[Conclusion]The image depth estimation model based on hybrid group dilated convolution is superior to existing methods,effectively improves the accuracy of depth estimation and can meet the depth estimation requirements of field corn plant images.关键词
深度估计/扩张卷积/自注意力/无监督学习/玉米植株图像Key words
Depth estimation/Dilated convolution/Self-attention/Unsupervised learning/Corn plant image分类
信息技术与安全科学引用本文复制引用
周云成,刘忠颖,邓寒冰,苗腾,王昌远..基于混合分组扩张卷积的玉米植株图像深度估计[J].华南农业大学学报,2024,45(2):280-292,13.基金项目
国家重点研发计划(2021YFD1500204) （2021YFD1500204）
辽宁省教育厅科学研究项目(LSNJC202004,LSNQN202022) （LSNJC202004,LSNQN202022）
