Lightweight crop and weed recognition method based on imporved DeepLabv3+
To achieve field crop and weed recognition on devices with limited storage resources and computational capabilities,a light-weight semantic segmentation network based on improved DeepLabv3+ is proposed.Firstly,MobileNet v2 is used as the feature extrac-tion backbone for DeepLabv3+,where the residual modules are replaced with dual-branch residual modules and the last two convolu-tional layers are removed to reduce the model parameters.Secondly,group-wise pointwise convolution is introduced in the Atrous Spa-tial Pyramid Pooling module,replacing standard convolutions with depthwise dilated convolutions,and performing multi-scale feature fusion on the convolved feature maps to enhance the extraction of deep features for crops and weeds.Lastly,the original non-linear ac-tivation functions are replaced with the Leaky ReLU activation function to improve segmentation accuracy.Experimental results show that the improved DeepLabv3+ achieves an mIOU(Mean Intersection over Union)of 86.75%with only 0.69M parameters,and a-chieves an FPS(Frames Per Second)of 98.Compared to the original DeepLabv3+ and three typical lightweight semantic segmentation networks,it has the lowest parameter count and the highest segmentation accuracy among the compared lightweight networks.
crop and weed identificationlightweightsemantic segmentationDeeplabv3+MobileNet v2multi-scale feature fusion
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