Abstract：The existing lane detection neural networks mainly adopt independent single frame image for detection, and therefore they cannot handle the complex and practical application scenarios, such as short-term occlusion of lane and light and shade changes of ground. To solve these problems, a parallel lane detection network based on image sequence is proposed according to the scene characteristics that the continuous images can be obtained in the normal driving process of vehicles. Firstly, a parallel feature extraction structure is designed. A single frame network with high accuracy is employed to extract the features of the current frame image. A lightweight multi-frame network is designed to extract the features of low resolution multi-frame sequential images. The cyclic neural network module is utilized to fuse the extracted sequential features to obtain multi-frame features. Then, the fusion module of single frame feature and multi-frame feature is designed, and the feature map of the lane line is output through upsampling network. The experimental results show that the objective detection accuracy and subjective effect of the proposed network are significantly improved.
朱威, 欧全林, 洪力栋, 何德峰. 基于图像序列的车道线并行检测网络[J]. 模式识别与人工智能, 2021, 34(5): 434-445.
ZHU Wei, OU Quanlin, HONG Lidong, HE Defeng. Parallel Lane Detection Network Based on Image Sequence. , 2021, 34(5): 434-445.
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2021, Vol. 34 
