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In order to improve the low lane departure warning rate due to the poor robustness of the traditional edge operator in lane feature extraction and the weak fitting ability of the traditional Hough transform curve, this paper proposed a lane departure warning method based on the threshold segmentation of the optimized maximum inter-class variance method (OTSU algorithm) and the sliding window method. Firstly, the genetic annealing algorithm was used to optimize and solve the optimal threshold of OTSU algorithm, and the Holistically-nested Edge Detection (HED) model was invoked to obtain the edge features of lane lines, and the area of interest was converted into an aerial view. Then, the sliding window method was utilized to slice the lane lines and the second-order polynomial fitting was carried out for the lane pixels in the window one by one. Finally, the lane departure warning and curve warning were given according to the relative position of the vehicle and the lane line. The test results show that the accuracy of the proposed method is 95.92%, and the detection rate can reach 34 ms/frame.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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