- 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
| Citation: | MA Qinglu, ZHANG Li, MA Lian, CAI Ke. Artificial Intelligence Adaptive Recognition Method for Tunnel Lane Lines in Autonomous Driving[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240070
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In view of the complex light environment problems such as sudden light changes, dimness, and glare in highway tunnels, a lane line recognition method, improved Hough & least squares (IHLS), based on embedded artificial intelligence (AI) is proposed. It used the improved Hough transform algorithm to carry out Hough transform for detecting straight lines at lane feature points, and employed the least squares method (LS) for curve fitting to identify curved lane lines. Real-time brightness detection and AI-based enhancement were performed on the captured image by embedding AI vision processing algorithm on the in-vehicle camera. The image was enhanced by the zero-reference deep curve estimation (Zero-DCE) model. The edge detection was performed by the improved Otsu method, and the dynamic region of interest (DROI) was divided by pixel statistics. The image was enhanced and smoothed by guided filtering to improve the accuracy of lane line recognition. The experiment on the proposed method was based on the Liupanshan Tunnel of Qingdao-Lanzhou Expressway (G22). Compared with the LS algorithm, the IHLS algorithm shows a mean-IoU (MIoU) index increased by 4.14%, average precision (AP) increased by 3.08%, and running time (RT) increased by 0.01 s. Compared with Hough transform, the algorithm presents an MIoU index increased by 4.18%, AP increased by 2.88%, and RT increased by 0.01 s. The IHLS algorithm embedded with AI visual processing solves the optical problems such as machine vision overexposure, color imbalance, and distortion, and realizes real-time recognition and tracking of lane lines in complex light environments.
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