Open AccessDeep Layer Aggregation with Cross Attention for Lane Detection
Author(s) -
Ming Lu,
Duan Liu,
Yongteng Sun,
Hao Duan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012010
Subject(s) - computer science , filter (signal processing) , encoder , segmentation , confusion , artificial intelligence , pattern recognition (psychology) , data mining , computer vision , psychology , psychoanalysis , operating system
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.