Semantic Segmentation과 탑뷰 변환을 통한 LDWS 성능 개선

초록

The Lane Departure Warning System (LDWS), a core function of Advanced Driver Assistance Systems (ADAS), prevents accidents by detecting unintended lane departures. Traditional methods based on edge detection or Hough transform are limited to illumination changes, lane fading, and complex roads. This study proposes integrating deep learning–based lane segmentation with Inverse Perspective Mapping (IPM) to improve LDWS performance. A SegNet and U-Net segmentation model was trained using the CamVid dataset, and the outputs were converted into top-view images, which were then evaluated with a ResNet18 classification model. The results show that U-Net achieved a higher level of segmentation accuracy, and IPM significantly improved classification accuracy. The combination of segmentation ROI and IPM yielded the best F1-score, confirming the effectiveness of the proposed approach for enhancing LDWS accuracy and robustness.

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