基于灰度均值的自适应FAST角点检测优化算法

An Adaptive FAST Corner Detection Optimization Algorithm Based on Grayscale Mean Value

光照不均、突变引起的灰度变化会影响图像特征检测效果。为此,设计一种基于灰度均值的自适应FAST-9-12角点检测算法。首先,利用特征点的延展性设计一种小面积双重检测模板,减少像素点与中心点的比较次数,提高区域正检率和检测速度;其次,依据图像局部灰度均值,在每个像素点检测模板内自适应调整阈值,避免灰度变化影响检测效果;最后,根据柔性非极大值抑制的思想设计角点半径抑制原则,筛选鲁棒性更强的角点。在Inria遥感影像数据集上的实验结果表明,FAST-9-12角点检测速度比FAST-12-16,FAST-9-16两种模板提高22%左右,因自适应阈值的提取方式不易受光照影响,检测准确率分别提高4.16和3.11个百分点,实现了图像特征快速和精准检测。

The grayscale changes caused by uneven illumination and sudden changes in illumination affect the detection effect of image features.Therefore an adaptive FAST-9-12 corner detection algorithm based on grayscale mean value is designed.Firstly a small-area double-detection template is designed based on the extensibility of feature points which reduces the number of comparisons between pixels and central points and improves the region positive detection rate and detection speed.Secondly based on the local grayscale mean value of the image the threshold is adaptively adjusted in the detection template of each pixel to avoid the impact of grayscale changes on the detection effect.Finally the corner radius suppression principle is designed according to the idea of flexible non-maximum suppression so as to screen more robust corners.The experimental results on the dataset of Inria remote sensing images show that the corner detection speed of FAST-9-12 is about 22%higher than that of FAST-12-16 and FAST-9-16 templates and since the extraction method of adaptive threshold is not easily affected by the illumination the detection accuracy is improved by 4.16 and 3.11 percentage points respectively.FAST-9-12 realizes rapid and accurate detection of image features.