Aiming at addressing the imbalance between the accuracy and real-time was of lane detection,a multi-lane detection network based on Lanenet and image enhancement technology was constructed to make use of feature information in the image and improve the detection accuracy and speed.Multi-scale Retinex algorithm was used to enhance the color of the input image and reduce noise.A bilateral multi-scale fusion network(BMFNet)was designed to realize the information interaction between shallow features and deep features and capture the context semantics.A new asymmetric convolution pyramid module(ACP)was used to fuse asymmetric convolution into atrous convolution layers with different dilated rates,so as to improve the feature extraction ability of the network and reduce the amount of computation.Experimental results show that compared with the exist-ing deep learning algorithms,the proposed method can effectively detect lane under occlusion and shadow conditions,and has higher accuracy,lower false positive(FP)and false negative(FN).
关键词
车道线检测/语义分割/图像增强/信息融合/池化金字塔/深度学习/非对称卷积
Key words
lane detection/semantic segmentation/image enhancement/information fusion/pooling pyramid/deep learning/asymmetric convolution
维普
万方数据