Algorithm of Fast Lane Identification Based on Overall Optimal Threshold
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摘要: 针对现有车道线识别算法难以自适应地匹配图像,在车辆过弯途中识别率低,鲁棒性和实时性较差的问题,提出并实现了一种整体最优阈值的快速车道线识别算法.该算法首先对图像进行自适应二值化分割;然后对图像中的感兴趣区域进行提取;提出逐行检索的方法进行车道线内侧特征点的筛选,从而得到实际车道的左右标志线参数以进行道路模型重建.结果表明:区别于以往常用的霍夫变换,此方法具有更好的实时性及准确性,可在车辆过弯途中为系统提供更多的有效信息.Abstract: Existing lane identification algorithm is difficult to match the image adaptively. Due to low recognition rate when the vehicle cornering,poor robustness and poor real-time,an algorithm of fast lane identification is proposed and implemented based on overall optimal threshold. The algorithm first processes image by adaptive binarization. Then,the region of interest is extracted. The method of progressive retrieval for screening feature points inside lane is proposed and real parameters of lane are obtained to reconstruct the road model.Resultsshow that being different from the previous common Hough transform,this method has better real-time performance and accuracy and can provide more useful information when the vehicle is cornering.
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Keywords:
- overall optimal threshold /
- machine vision /
- lane detection /
- road recognition /
- intelligent vehicle
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