电子测量与仪器学报

周勇,刘泓滨,侯亚东.复杂城市交通场景下的自动驾驶语义分割方法[J].电子测量与仪器学报,2024,38(4):241-247

复杂城市交通场景下的自动驾驶语义分割方法

Automatic driving semantic segmentation method forcomplex urban traffic scene

DOI：

英文关键词:semantic segmentation automatic driving feature enhancement module multi-scale feature pyramid similar features

基金项目:

作者	单位
周勇	昆明理工大学机电工程学院昆明650500
刘泓滨	昆明理工大学机电工程学院昆明650500
侯亚东	昆明理工大学机电工程学院昆明650500

Author	Institution
Zhou Yong	Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Liu Hongbin	Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Hou Yadong	Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

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中文摘要:

多尺度特征金字塔可以缓解语义分割在复杂交通场景下通常存在漏分割、错误分割、边界分割不清晰等问题，但现有的多尺度特征金字塔在获取丰富的语义信息时，不得不下采样特征图，牺牲空间细节信息，而这导致了最终的分割结果仍然精度受限。针对该问题，本文提出了特征强化模块，使得有利于正确分类像素的相似特征在下采样过程之前得到基于不同矢量间余弦相似度的进一步加强，降低下采样带来的负面影响。另外，结合空洞卷积和条带卷积原理，本文对大卷积核进行了改造，并构建新的多尺度特征金字塔模块，以获取尺度不同且具备更大感受野的语义信息。该分割方法实时高效，能够满足自动驾驶语义分割要求，在VOC2012数据集上的实验表明，所提出的方法分割结果mIoU达到了74.36%，FPS达到了43，优于目前的主流语义分割方法。

英文摘要:

The multi-scale feature pyramid can alleviate the problems of semantic segmentation in complex traffic scenes, such as missing segmentation, wrong segmentation and unclear boundary segmentation. However, the existing multi-scale feature pyramid has to downsample the feature maps and sacrifice the spatial detail information for rich semantic information, leading to the limited accuracy of the final segmentation result. Aiming at this problem, a feature enhancement module is proposed to further reinforce similar features based on cosine similarity between different vectors before downsampling, alleviating the negative influence of downsampling. In addition, combined with the principle of dilated convolution and strip convolution, the large convolution kernel is modified to build a new multi-scale feature pyramid module for semantic information with different scales and larger receptive fields. The proposed segmentation method is real-time and efficient, and can meet the requirements of automatic driving. Experiments on the VOC2012 dataset show that the mIoU of the proposed method reaches 74.36%, and the FPS reaches 43, which is superior than the current prevailing semantic segmentation methods.

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