基于改进卷积神经网络的平面地图道路模糊推理分割方法

由于平面地图呈现方式单一且有限,为提升其多样性需准确分割平面地图中的道路区域。提出一种基于改进CNN(convolutional neural network)平面地图道路和模糊推理分割的方法。选取两个道路信息丰富的数据库,实验选取百度地图(Baidu)数据库和高德地图(Amap)数据库,标记得到含标签信息的像素训练集;用Sigmoid分割目标函数代替复杂的Softmax函数分别训练得到Baidu-CNN模型和Amap-CNN模型;对得到的像素点概率进行非线性映射,构建模糊推理系统;将非线性映射后均匀分布的像素点概率输入模糊推理系统,判断像素点属于道路区域的概率,得到道路分割结果。结果表明:所提算法得到的平面地图道路分割模型较传统算法分割效果更好;准确率可以达到94.49%;单张平面地图的道路分割速度可达到5 s。

Planar Map Road Fuzzy Segmentation Method Based on Improved Convolutional Neural Network

Planar map showing roads information are always incomplete. In order to improve the diversity of planar maps, it is necessary to accurately segment the road regions. Therefore, a method for improving convolutional neural networks is proposed to segment the planar map region. This experiment selects two road information-rich databases, namely Baidu database (Baidu) and Gaode database (Amap) , and then mark the pixel training set with the tag information. Using the Sigmoid segmentation objective function instead of the complex Softmax function, the Baidu-CNN model and the Amap-CNN model are trained respectively.And adjust the pixel probability with nonlinear mapping, so the fuzzy inference system is constructed. The nonlinearly mapped pixel point probability is input into the fuzzy inference system to determine the probability that the pixel point belongs to the road region, and the road segmentation result is obtained. The results show that the planar map road segmentation model obtained by the proposed algorithm has better segmentation effect. The accuracy rate can reach 94.49%, and the road segmentation speed of a single planar map can reach 5 seconds.