基于双特征提取网络的复杂环境车道线精准检测

doi:10.13229/j.cnki.jdxbgxb.20221129

Abstract

Abstract:

The existing lane detection methods have the problem of low detection accuracy due to fuzzy details in a complex environment. Therefore， this paper proposes an accurate lane detection algorithm based on a double feature extraction network in a complex environment. Firstly， a double feature extraction network is built to obtain feature maps of different scales， extract more effective features， and improve the feature extraction ability of the model in complex environments. Besides， a cross-channel joint attention module is constructed to improve the attention of the model to lane details and suppress useless information. Finally， combined with the improved void space pyramid pooling module， the receptive field is enlarged to improve the utilization of context information of the model， to strengthen the detection ability. The experimental results show that the F₁-measure of the proposed algorithm on CULane dataset reaches 72.43%， which is 4.03% higher than that of the mainstream UFSD algorithm. When detecting lane lines in complex scenes， the detection effect of the proposed method is significantly improved， which has been proven to be able to meet the needs of practical applications.

Key words: computer application, lane detection, double feature extraction, multi-scale, combined attention mechanism

CLC Number:

TP391.4

Yun-zuo ZHANG,Yu-xin ZHENG,Cun-yu WU,Tian ZHANG. Accurate lane detection of complex environment based on double feature extraction network[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(7): 1894-1902.

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算法模型	FastDraw^［24］	SCNN^［23］	SAD^［25］	UFSD^［20］	CurveLanes^［26］	本文
综合	-	71.60	70.80	68.40	71.40	72.43
正常	85.90	90.60	90.10	87.70	88.30	90.72
拥挤	63.6	69.70	68.80	66.00	68.60	72.28
强光	57.0	58.50	60.20	58.40	63.20	64.56
阴影	59.9	66.90	65.90	62.80	68.00	68.89
无车道线	40.6	43.40	41.60	40.20	47.90	43.38
箭头	79.4	84.10	84.00	81.00	82.50	86.46
弯道	65.2	64.40	65.70	57.90	66.00	64.12
交叉路口	7013	1990	1998	1743	2817	1804
夜晚	57.8	66.10	66.00	62.10	66.20	66.53

基础模型^［20］	DFE-Net	CBAM^［21］	AUAM	ASPP^［27］	ASPP*	F₁-measure
√						68.40
√	√					69.97
√	√	√				70.03
√	√		√			70.56
√	√		√	√		71.92
√	√		√		√	72.43

基础模型	DFE-Net	AUAM	ASPP*	F₁-measure
√				68.40
√	√			69.97
√	√	√		70.56
√	√	√	√	72.43

Accurate lane detection of complex environment based on double feature extraction network

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