To address the high computational complexity and large computational load of IIE-SegNet,this paper proposes an improved method based on IIE-SegNet.VGG16 trained in ImageNet and multiscale atrous spatial pyra-mid pooling(MASPP)module are introduced into the encoding module to obtain abundant coding information.In the decoding structure,the global add average(GAA)module is designed to solve the problem of heavy computa-tion of IIE-SegNet.Focal loss function is analyzed to solve the problem of positive and negative sampling imbal-ance.The experimental result shows that compared with IIE-SegNet,on the PASCAL VOC 2012 dataset,our net-work achieves faster segmentation,with an average of 0.6 s faster per iteration,a reduction in the average time to test a single image by 0.94 s,and an increase in MIoU 2.1%.On the expanded PASCAL VOC 2012(Exp-PAS-CAL VOC 2012)data set,the semantic segmentation speed is faster,an average of 0.4 s faster per iteration,and the average time to test a single image is reduced by 0.92 s;furthermore,our network exhibits higher accuracy and improvements in the MPA and MIoU by 2.6%and 2.8%,respectively.In particular,for small-scale target seg-mentation,the boundary is clearer,and the performance is greatly improved.
维普
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