Workshop Scene Segmentation Method Basedon Improved U-Net
In order to better analyze the workshop environment and solve the problems of low semantic segmentation accuracy caused by few sample samples,many categories and large scale changes of the target pixel-level segmentation task of the workshop,an improved U-Net workshop scene segmentation model was designed.The improved model used Rep-VGG lightweight backbone network,and introduced the pyramid splitting attention mechanism in the sampling stage of U-net to increase the feature representation ability and reasoning speed of the model.In model training,Dice-Cross Entropy combination loss function was adopted to solve the problem of difficult training caused by unbalanced samples.Experimental data showed that the model could achieve fast,lightweight and high-precision segmentation on the self-built small-sample workshop dataset.
semantic segmentationlightweight neural networkattention mechanismcombined loss functiondeep learning
万方数据
维普
