基于残差网络的弯道增强车道线检测方法

doi:10.13229/j.cnki.jdxbgxb20210618

Abstract

Abstract:

In this paper, the main purpose is to improve the curve detection effect, and considering the detection speed comprehensively, a curve lane detection method based on residual network is proposed. In this method， the residual network is used as the main framework and the curve enhancement is realized by adding the curve structure constraints to the loss function. On the other hand， in order to reduce the complexity of the model， the weights pruning technique is used to reduce the model. The result shows that the curve enhancement strategy proposed in this paper can not only effectively improve the algorithm performance in the curve scene， but also has little impact on the detection performance of straight lanes. After adding the sparse pruning strategy， the algorithm greatly reduces the computation time without significant performance degradation， which is more in line with actual production requirements.

Key words: technology of computer application, lane detection, residual network, loss function, pruning

CLC Number:

TP391.4

Xiao-hu SHI,Jia-qi WU,Chun-guo WU,Shi CHENG,Xiao-hui WENG,Zhi-yong CHANG. Residual network based curve enhanced lane detection method[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 584-592.

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指标	模型	Tusimple	弯道测试集
Accuracy	UFLD	0.9581	0.9462
Accuracy	弯道增强	0.9579	0.9572
Precision	UFLD	0.8095	0.7789
Precision	弯道增强	0.8104	0.7987
Recall	UFLD	0.9538	0.9229
Recall	弯道增强	0.9554	0.9533
F1	UFLD	0.8757	0.8448
F1	弯道增强	0.8769	0.8692

指标	模型	无剪枝	有剪枝
Accuracy	UFLD	0.9462	0.9340
Accuracy	弯道增强	0.9572	0.9505
Precision	UFLD	0.7789	0.7688
Precision	弯道增强	0.7987	0.7886
Recall	UFLD	0.9229	0.9099
Recall	弯道增强	0.9533	0.9375
F1	UFLD	0.8448	0.8334
F1	弯道增强	0.8692	0.8566

算法	准确率
UFLD	0.9581
FastDraw	0.9520
编-解码卷积网络	0.9550
本文方法（带剪枝策略）	0.9505
本文方法（无剪枝策略）	0.9579

Residual network based curve enhanced lane detection method

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