摘要
为了解决车辆行驶中面对各种复杂环境车道线检测算法精度不高的问题,提出一种基于改进的UFS网络检测算法(Ultra Fast Structure-aware Deep Lane Detection,UFS).首先,采用改进的Gamma校正对待检路面图像进行校正,降低光照、阴影等的影响,以提升夜间图像纹理特征。然后引入非局部神经网络模块(Non-Local Block),充分提取图像全局特征,以提高检测可靠性。最后对改进后的算法使用Tusimple、CULane数据集进行测试。结果表明:改进后的模型在物体遮挡、光照变化、阴影干扰等复杂场景下,提升了对复杂噪声与多元场景的处理能力,车道分割的准确率有所改善,具有较好的鲁棒性。
In order to solve the problem of poor real-time and low precision of lane line detection algorithm in various complex environments,an improved UFOS network detection algorithm(Ultra Fast Structure-aware Deep Lane Detection,UFS)is proposed.First of all,the improved gamma correction is used to correct the inspection road image,reduce the influence of lighting,shadows,etc.,in order to enhance the texture characteristics of the night image.Then a non-local neural network module(Non-Local Block)is introduced to extract the global characteristics of the image in order to improve detection reliability.Finally,the improved algorithm is tested using TuSimple and CULane datasets.The results show that the improved model improves the processing power of complex noise and multiple scenes under complex scenes such as object masking,lighting change and shadow interference,and the accuracy of lane segmentation is improved,and it is more robust.
作者
石磊
范英
苏伟伟
闫浩
SHI Lei;FAN Ying;SU Wei-wei;YAN Hao(Department of Transportation and Logistics,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《太原科技大学学报》
2024年第2期160-165,共6页
Journal of Taiyuan University of Science and Technology
基金
山西省重点研发计划(201903D121176)。
