基于传统视觉技术的车道检测系统，实现实时鲁棒识别。
Lane Detection System for Driver Assistance in Vehicles
- 采用图像处理流水线，结合校准与透视变换提升精度
- 滑动窗口与梯度/颜色分割结合，支持多场景车道识别
- 适合自动驾驶与辅助驾驶系统，尤其在复杂光照下表现佳
本文提出一种面向常规与自动驾驶车辆的车道检测系统。系统基于传统计算机视觉技术，强调实时性与鲁棒性，可在路面磨损、天气变化等不利条件下运行。方法包括相机标定、畸变校正、透视变换和二值化图像生成。通过滑动窗口技术与基于梯度及颜色通道的分割实现车道精准识别，适用于多种道路场景。实验表明，系统在不同光照条件与路面状况下均能有效检测与跟踪车道。但在强烈阴影与急弯等极端情况下仍存在挑战。尽管如此，传统视觉方法在驾驶辅助与自主导航中展现出显著潜力，未来仍有优化空间。
原文摘要 · Abstract (English)
This work presents the development of a lane detection system aimed at assisting the driving of conventional and autonomous vehicles. The system was implemented using traditional computer vision techniques, focusing on robustness and efficiency to operate in real-time, even under adverse conditions such as worn-out lanes and weather variations. The methodology employs an image processing pipeline that includes camera calibration, distortion correction, perspective transformation, and binary image generation. Lane detection is performed using sliding window techniques and segmentation based on gradients and color channels, enabling the precise identification of lanes in various road scenarios. The results indicate that the system can effectively detect and track lanes, performing well under different lighting conditions and road surfaces. However, challenges were identified in extreme situations, such as intense shadows and sharp curves. It is concluded that, despite its limitations, the traditional computer vision approach shows significant potential for application in driver assistance systems and autonomous navigation, with room for future improvements.
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