• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 55 Issue 2
Mar.  2020
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Article Contents
TIAN Sheng, ZHANG Jianfeng, ZHANG Yutian, XU Kai. Lane Detection Algorithm Based on Dilated Convolution Pyramid Network[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026
Citation: TIAN Sheng, ZHANG Jianfeng, ZHANG Yutian, XU Kai. Lane Detection Algorithm Based on Dilated Convolution Pyramid Network[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026

Lane Detection Algorithm Based on Dilated Convolution Pyramid Network

doi: 10.3969/j.issn.0258-2724.20181026
  • Received Date: 06 Dec 2018
  • Rev Recd Date: 21 Feb 2019
  • Available Online: 07 Mar 2019
  • Publish Date: 01 Apr 2020
  • In order to meet the accuracy and timeliness requirements of advanced driver-assistance system in lane detection, the improved ResNet50 network as the basic model to extract the features of the local laneline is proposed. Given that the dilated convolution can exponentially expand the receptive field, the dilated convolutional pyramid module is designed to completely extract the laneline features on different scales. The idea of anchor grid is proposed, by which the output is divided into a set of grids, and each grid is classified and analyzed by regression. After non-maximum suppression and other post-processes, a set of laneline marking points are output by the model. Experimental results show that if the model is tested with CULane multi-scene dataset and the intersection-over-union (IoU) threshold is 0.3, the comprehensive evaluation index F-measure reaches 78.6% and the detection rate reaches 40 frames per second. With similar evaluation indexes, the detection rate of the proposed model is much higher than that of the spatial convolutional neural networks (SCNN) model, and its detection performance in difficult scenes such as dazzle light and curve is more desirable.

     

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