首页|基于级联残差的弯道增强车道线检测

基于级联残差的弯道增强车道线检测

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  • 万方数据
  • 维普
为增强车道线弯道场景的检测效果,提出一种结合级联改进残差块和动态蛇形卷积模块的特征提取网络,优化特征提取,并采用基于混合锚点的位置选择与分类方法,加速准确定位和分类.特征提取网络中包含多尺度特征融合残差块以及集成空间通道重组卷积的改进残差块,深度捕捉车道线的精细特征.动态蛇形卷积模块通过动态调整卷积核,增强网络在弯道区域的识别性能.在Tusimple数据集中,准确率可达95.88％,误检率和漏检率分别为2.84％和4.25％,表明具有较高检测性能,FPS为50.7,满足自动驾驶汽车对实时检测的需求.在弯道场景数据集Tusimple_Curve中,本方法相较于UFLD V2算法,准确率实现了 0.23％的提升,误检率和漏检率分别降低了 0.22％和0.36％,进一步证实了其在弯道场景中的有效性.
Cascaded residual-based curve enhancement for lane detection
To enhance the detection effect in curved lane scenarios,a feature extraction network combining a cascaded improved residual block and a snake serpentine convolution module is proposed to optimize feature extraction,along with a mixed-anchor-based position selection and classification method to accelerate accurate localization and classification.The feature extraction network includes multi-scale feature fusion residual blocks and improved residual blocks that integrate spatial channel reorganization convolution,deeply capturing the fine features of lane lines.The snake serpentine convolution module enhances the network's recognition performance in curved areas by dynamically adjusting the convolution kernels.On the Tusimple dataset,this method achieves an accuracy of 95.88％,with a false detection rate and a missed detection rate of 2.84％and 4.25％,respectively,indicating high detection performance.The frames per second(FPS)is 50.7,meeting the real-time detection requirements of autonomous driving cars.In the curved scenario dataset Tusimple_Curve,compared to the UFLD V2 algorithm,this method achieves a 0.23％improvement in accuracy,and the false detection rate and missed detection rate are reduced by 0.22％and 0.36％,respectively,further confirming its effectiveness in curved scenarios.

autonomous drivinglane detectionmulti-scale featuresdynamic snake convolutionanchor classificationcurve detection

潘玉恒、李慧洁、鲁维佳、李国燕、丛佳、李飞腾、张金燕

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天津城建大学计算机与信息工程学院,天津 300384

自动驾驶 车道线检测 多尺度特征 动态蛇形卷积 锚点分类 弯道检测

国家自然科学基金项目天津市科技计划项目天津市科技计划项目天津市科技计划项目天津市教委科研计划项目天津市研究生科研创新项目天津城建大学教育教学改革与研究重点项目天津城建大学教育教学改革与研究重点项目

6220416820YDTPJC0016021YDTPJC0078023YDTPJC004502019KJ1012022SKYZ033JG-ZD-22035JG-ZD-22038

2024

10.16251/j.cnki.1009-2307.2024.07.005

测绘科学
中国测绘科学研究院

测绘科学

CSTPCD北大核心
影响因子：0.774
ISSN：1009-2307
年,卷(期)：2024.49(7)