基于编解码器模型的车道识别与车辆检测算法

doi:10.12052/gdutxb.180175

广东工业大学学报 ›› 2019, Vol. 36 ›› Issue (04): 36-41.doi: 10.12052/gdutxb.180175

基于编解码器模型的车道识别与车辆检测算法

谢岩, 刘广聪

广东工业大学计算机学院, 广东广州 510006

收稿日期:2018-12-24 出版日期:2019-06-18 发布日期:2019-05-31
作者简介:谢岩(1993-),男,硕士研究生,主要研究方向为无人驾驶.
基金资助:
广州市科技计划项目（201508020030）

Lane Recognition and Vehicle Detection Algorithm Based on Code-model

Xie Yan, Liu Guang-cong

School of Computers, Guangdong University of Technology, Guangzhou 510006, China

Received:2018-12-24 Online:2019-06-18 Published:2019-05-31

摘要/Abstract

摘要： 针对无人驾驶车辆环境感知问题，通过编码器提取共享图像特征，再通过解码器来实现语义分割、分类和目标检测模块，并应用在车道识别和车辆检测上.在无人驾驶中，任务的实时性非常关键，这种共享编码器模型能一定程度上提高任务实时性.实验结果表明，该模型的语义分割在KITTI数据集上的平均精度达到93.89%，比最优性能提升0.53%，联合检测速度达到25.43 Hz.

关键词: 无人驾驶, 编解码器模型, 语义分割, 目标检测, 带孔卷积

Abstract: Aiming at the problem of environment perception of self-driving, this paper semantic segmentation, classification and target detection module are realized by the code model, which is applied to lane recognition and vehicle detection. Shared image features are extracted by encoder, and three different functions are realized by decoder. This Shared encoder model can improve the real-time performance of tasks. In self-driving, the real-time performance of tasks is the key. Experimental results show that the average precision of semantic segmentation of this model on KITTI dataset reaches 93.89%, which is 0.53% higher than the optimal performance, and the joint detection speed reaches 25.43 Hz.

Key words: self-driving, code-model, semantic segmentation, target detection, atrous convolution

中图分类号:

TP391.4

谢岩, 刘广聪. 基于编解码器模型的车道识别与车辆检测算法[J]. 广东工业大学学报, 2019, 36(04): 36-41.

Xie Yan, Liu Guang-cong. Lane Recognition and Vehicle Detection Algorithm Based on Code-model[J]. Journal of Guangdong University of Technology, 2019, 36(04): 36-41.

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基于编解码器模型的车道识别与车辆检测算法

Lane Recognition and Vehicle Detection Algorithm Based on Code-model

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