| Title |
Lane Line Detection Based on Improved Semantic Segmentation in Complex Road Environment |
| ID_Doc |
41930 |
| Authors |
Ma, CW; Luo, DA; Huang, H |
| Title |
Lane Line Detection Based on Improved Semantic Segmentation in Complex Road Environment |
| Year |
2021 |
| Published |
Sensors And Materials, 33, 12 |
| DOI |
10.18494/SAM.2021.3544 |
| Abstract |
With the concepts of smart city and smart travel and the rapid development of modern sensors, artificial intelligence, and other modern technologies, automatic driving technology that can effectively solve road congestion and ensure driving safety has become the main direction of future industry development. Accurate lane line technology is a fundamental technology for realizing autonomous driving. However, in actual road environments, lane lines are often detected with a low accuracy because of various factors, including light intensity changes and lane line obstruction, which greatly affect the safety of autonomous driving. To address the current challenges in lane line detection, in this study, we propose a lane line detection model based on improved semantic segmentation for complex road scenarios, such as lane line occlusion, mutilation, and shadowing. The Visual Geometry Group-Special Convolutional Neural Network (VGG-SS) proposed in this paper, which is based on the VGG-16 network, introduces a self-attentive distillation model and a spatial convolutional neural network (SCNN) model. Empirical results show that the proposed model outperforms the current semantic segmentation models, achieving better detection effects and a higher F1 value of 82.6 in complex road scenarios. The results prove that the proposed method can effectively improve the detection accuracy of lane lines. |
| Author Keywords |
smart city; autonomous driving; complex road environment; lane line detection; semantic segmentation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000737326000001 |
| WoS Category |
Instruments & Instrumentation; Materials Science, Multidisciplinary |
| Research Area |
Instruments & Instrumentation; Materials Science |
| PDF |
https://sensors.myu-group.co.jp/sm_pdf/SM2780.pdf
|