基于HSV颜色空间的快速边缘提取算法

Fast Edge Extraction Algorithm Based on HSV Color Space

针对当前无人机障碍物检测能力较弱的问题,基于图像快速边缘提取算法,提出一种改进型局部二值模式(I-LBP)算子来增强边缘提取效果.在HSV (Hue, Saturation, Value)颜色空间中,利用I-LBP算子描述像素点的局部纹理特征,并采用Hausdorff距离确定边缘点,最后用矩形框圈出障碍物轮廓.该算法根据模糊集理论对传统LBP算子进行改进,通过将简单的二值化描述方式扩充到三维向量,增强了LBP算子对局部纹理特征的描述能力及其抗噪声的能力.利用MATLAB软件进行仿真验证的结果表明:I-LBP算子具有良好的稳健性,在光照条件较差并有噪声污染的情况下依旧可以准确地识别障碍物;同时,I-LBP算子还具有良好的实时性,可以满足无人机的避障要求.

In order to improve the ability of unmanned aerial vehicle(UAV) to detect obstacles, a fast edge detection algorithm is studied and an improved local binary pattern(I-LBP) operator is proposed to enhance the edge extraction effect. In HSV(Hue, Saturation, Value) color space, the I-LBP operator is used to describe the local texture features of pixels. The Hausdorff distance is used to confirm the edge pixels. And the contour of the obstacle is framed in the rectangular box. The algorithm improves the traditional LBP operator according to the fuzzy set theory. The simple binary description of the local texture features is extended to three-dimensional vector. It enhances the LBP operator’s ability to describe the local texture features and is noise-robust.The simulation verification is carried out with MATLAB. The results show that the I-LBP operator has good robustness and can identify obstacles under the condition of poor illumination and noise pollution. It also has high real-time ability, which can meet the requests of UAV in avoiding obstacles.