基于改进LSD和AP聚类的路径边缘识别策略

起重机金属结构攀爬机器人的路径边缘识别策略分为 3 个步骤。①图像预处理，利 用改进的过颜色算子进行灰度化；②使用基于支持向量机(SVM)最优分类线的方法确定梯度阈 值，并增设主方向角约束，改进线段分割检测(LSD)算法，得到直线段检测图像；③对直线段进 行特征提取，构建聚类数据集，基于数据集动态变化的特点，将基于先验信息的判别模型与近邻 传播(AP)聚类算法相结合，改进 AP 聚类算法，对直线段进行聚类，筛选出构成路径边缘的直线 段，并拟合得到最终的路径边缘线。实验结果表明，相较 AP 聚类和其他聚类算法，改进 AP 聚 类算法的筛选准确率最高；基于改进 LSD 和 AP 聚类的路径边缘识别策略的识别成功率为 96%， 且满足精度和实时性要求。

Path Edge Recognition Strategy Based on Improved LSD and AP Clustering

The path edge recognition strategy of the crane metal structure climbing robot is divided into three steps. Firstly, image pre-processing which means using the improved over-color operator for grayscale. Secondly, the gradient threshold is determined by the method based on the optimal classification line of the support vector machine, in addition, main direction angle constraint is added to improve line segment detector (LSD) algorithm, and obtain the straight line detection image for clustering. Thirdly, the clustering data set is constructed by the feature extraction of straight line segments. Based on the dynamism of the data set feature of, the improved AP clustering algorithm is established by combining the prior information based discriminant model with the affinity propagation (AP) clustering algorithm to cluster the line segments and screen out the line segments constituting the edge of the path, and obtain the final path edge line by fitting. The experimental results show that compared with the traditional AP clustering and other clustering algorithms, the improved AP clustering algorithm has the highest screening accuracy for path edge lines. The recognition success rate of path edge recognition strategy based on improved LSD and AP clustering is 96% which meets the accuracy and real-time requirements.