Abstract：In order to solve the problems of poor real-time and low accuracy performance of lane detection algorithms in vehicle-mounted embedded devices with limited computing resources，a lightweight lane detection algorithm（SegLaneNet）with semantic segmentation is proposed.First，by simplifying the parallel hollow convolution branches and adding jump connection structures，a new atrous spatial pyramid pooling module（ASPP-tiny）is proposed.And then pruning the up-sampling and down-sampling convolution in the autoencoder，a new lightweight fully convolutional semantic segmentation algorithm SegLaneNet is applied to lane line detection.Finally，compared with Baseline algorithm，the accuracy of the SegLaneNet algorithm on the TuSimple lane line detection challenge dataset has been improved by about 2%，the false positive（FP）has been reduced by more than 3%，the false negative（FN）has been reduced by about 2%，and the running speed on the GPU server has reached 165 frames per second（FPS），while The computing speed in embedded devices reaches 16 frames per second（FPS）.The test results show that the light-weight lane detection algorithm with semantic segmentation can meet the real-time and accurate lane detection tasks of vehicle-mounted embedded devices.
2021, Vol. 42 
