融合通道注意力机制和深度编解码卷积网络的道路场景分割  

Road Scene Segmentation Based on Deep Convolutional Encoder-decoder Network with Channel Attention Mechanism

作　　者：黄泽华 丁学明[1] HUANG Ze-hua;DING Xue-ming(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《小型微型计算机系统》2021年第11期2362-2367,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61673277)资助.

摘　　要：针对深度编解码卷积网络在道路场景分割中没有考虑对各卷积特征图通道依赖性的问题,提出了一种融合通道注意力机制的深度编解码卷积网络,并将通道注意力机制改进为双路通道注意力机制.该方法保留了原有通道注意力机制能优化背景信息的优点,同时增加另一路通道用来收集到难区分物体之间重要的特征,从而获得详细的通道注意力.实验结果表明,对于道路场景图像,融合双路通道注意力机制的深度编解码卷积网络进一步提高了分割性能,其中在评价指标准确率和平均交并比分别提高了约7个百分点和8个百分点.To address the problem that deep convolutional encoder-decoder network do not take into account the channel dependence on each convolutional feature map in road scene segmentation,a deep convolutional encoder-decoder network that incorporates the channel attention mechanism was proposed,and the channel attention mechanism was improved to a dual channel attention mechanism.The method retained the advantage that the original channel attention mechanism can optimize background information,while adding another channel for collecting important features between the difficult to distinguish objects to obtain detailed channel attention.For the road scene images,the experimental results show that the deep convolutional encoder-decoder network with the dual channel attention mechanism further improves the segmentation performance,which improves the evaluation accuracy and intersection over union by about 7 and 8 percentage points respectively.

关 键 词：道路场景分割 深度编解码卷积网络 通道注意力机制 分割性能 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]