多级特征融合下的高精度语义分割方法

High precision semantic segmentation based on multi-level feature fusion

为解决图像语义分割中边缘分割模糊与小目标物体分割不精细的问题,提出了一种高精度语义分割方法。该方法利用MobileNetV3网络,提取多级的浅层轮廓特征和深层语义特征,通过PSP-Net模型中的金字塔池化模块和上采样操作,将多级浅层的轮廓特征信息与深层的语义特征信息进行融合,实现了多级特征融合的高精度图像语义分割。在Nyu-V2数据集上实验的结果表明,该算法明显提高了对小目标特征的描述能力。在Pascal-VOC2012数据集上进一步验证了该算法的泛化性。与3种主流方法进行了实验对比,该算法的分割精度相比Deeplabv3+提高了2.1%,相比PSP-Net提高了5.1%,相比SEG-Net提高了10.9%。

In order to solve the problems of fuzzy edge segmentation and imprecise segmentation of small objects in image semantic segmentation,a high-precision semantic segmentation method was proposed.The method used MobileNetV3 network to extract multi-level shallow contour features and deep semantic features,and then fused the multi-level shallow contour feature information with the deep semantic feature information by Pyramid pooling module and up-sampling operation in PSP-Net model,so as to realize the high-precision semantic segmentation with multi-level feature fusion.The experimental results on the Nyu-V2 dataset show that the proposed algorithm can improve the description ability of small target features.The generalization of the proposed algorithm is further verified on the Pascal-VOC2012 dataset.Experimental comparison with three mainstream methods shows that the segmentation accuracy of the proposed algorithm increases by 2.1%compared with Deeplabv3+,by 5.1%PSP-Net and by 10.9%SEG-Net.