Research Article
Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks
Table 5
Result of comparison lane detection algorithm.
| Method | Algorithm | Average accuracy (%) | Average processing time (second) | Environment system |
| Traditional method | Hough transform [33] | 95.70 | 0.06540 | Intel Core i7-6700K CPU@ 4 GHz | Traditional method | RANSAC + HSV [34] | 86.21 | 0.50000 | Intel Core i7-4700 CPU@ 2.40 GHz | Horizontal filter + Otsu | [35] | 83.00 | 0.013 | CPU Intel 3.30 GHz | Traditional method | Sliding window KITTI [4] | 84.00 | 0.00318 | Intel Core i5 5200U CPU@ 2.20 GHz | Deep learning | FastDraw ResNet [36] | 95.00 | 0.06533 | NVIDIA GeForce GTX 1080, GPU | Our proposal | HSL + Sobel filter + SWS KITTI [4] | 85.13 | 0.08620 | Intel Core i3-6100U, CPU@ 2.3 GHz | Our proposal | HSL + Sobel filter + SWS TuSimple | 97.91 | 0.08500 | Intel Core i5-9300H, CPU@ 2.4 GHz | Our proposal | HSL + Sobel filter + SWS TuSimple | 97.91 | 0.0021 | GPU GeForce GTX TITAN X |
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