一种结合深度学习和随机森林的地平线检测方法

A Horizon Detection Method Based on Deep Learning and Random Forest

已有地平线检测方法检测效果受环境影响较大,计算复杂度较高。提出了一种基于深度学习与随机森林相结合的地平线检测方法。利用深度学习模型进行深度特征提取,将得到的深度特征用于随机森林训练,采用随机森林回归投票方式得到地平线检测结果。仿真结果表明,所提方法检测效果较好。不仅在笔直的道路上检测结果与真实值比较相近,而且在阴影区域以及弯道中的预测值也基本与真实值重合,表明该方法鲁棒性强,能够很好的用于复杂道路场景中的地平线检测。

The detection effect of existing horizon line detection methods is greatly affected by the environment, and the computational complexity is high. Aiming at the problem of horizon line detection in complex road scene in real-life, a horizon line detection method based on deep learning and random forest is proposed. The deep learning model is used to extract the depth features, then the obtained depth features are used for random forest training. The results of horizon line detection are obtained by random forest regression-voting. The simulation results show that this method has good detection effect. The detection results are not only similar to the real value on the straight road, but also are basically coincident with the true value in the shadow and the curve area. It shows that the method is robust. It can be used to detect the horizon line in complex road scene.