基于区块自适应特征融合的图像实时语义分割

Real-time Image Semantic Segmentation Based on Block Adaptive Feature Fusion

近年来结合深度学习的图像语义分割方法日益发展,并在机器人、自动驾驶等领域中得到应用.本文提出一种基于区块自适应特征融合(Block adaptive feature fusion,BAFF)的实时语义分割算法,该算法在轻量卷积网络架构上,对前后文特征进行分区块自适应加权融合,有效提高了实时语义分割精度.首先,分析卷积网络层间分割特征的感受野对分割结果的影响,并在跳跃连接结构(SkipNet)上提出一种特征分区块加权融合机制;然后,采用三维卷积进行层间特征整合,建立基于深度可分离的特征权重计算网络.最终,在自适应加权作用下实现区块特征融合.实验结果表明,本文算法能够在图像分割的快速性和准确性之间做到很好的平衡,在复杂场景分割上具有较好的鲁棒性.

Recently,image semantic segmentation has made great progress with deep learning,which benefits robotics and automatic driving vehicle.This paper proposes a real-time semantic segmentation algorithm based on block adaptive feature fusion(BAFF).Under the framework of a light convolutional network,a block adaptive feature fusion algorithm is proposed in the context-embedding module,to improve the accuracy of real-time semantic segmentation.First,the problem caused by the different size of receptive field in layers is analyzed,and a feature fusion mechanism with block weight is presented on SkipNet.Then,layers'feature integration is carried on by three-dimension convolution.The feature-weights are calculated by an additional network with depthwise-separable-convolutions(DSC).Finally,the features are fused under adaptive weights.Experiments show that this method obtains excellent segmentation results with a good balance between rapidity and accuracy and owns robustness on segmentation of complex scenes.