Deep Layer Aggregation with Cross Attention for Lane Detection

Abstract

As an important part of driverless vehicle research and high-precision map making, lane detection technology needs to be improved urgently. However, it is difficult for current networks to obtain long-distance semantic information, which leads to the confusion of categories in narrow lanes. This paper adds cross attention based on deep layer aggregation to obtain long-distance semantic information, and constructs time series filter to filter in time domain, the method is simple and robust. Our encoder is based on resnet-50. According to the experimental results, slightly changing resnet-50 can achieve better results. Our method is evaluated on Baidu ApolloSpace land segmentation dataset, increases 3.4% relative to DeeplabV3+, and cross attention with time series filter contribute more than 1% mIoU accuracy.