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

Abstract

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

CLC Number:

TP391.4

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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