ORB（oriented FAST and rotated BRIEF）特征检测算法，在模糊场景和光照变化剧烈的环境中，容易使提取的特征点的数量和匹配的正确率出现巨大的差异，同时，在图像物体的拐角处也容易出现特征点的堆叠。针对这一情况，提出了一种改进的ORB特征检测算法。首先使用多尺度视网膜增强（multi-scale retinex, MSR）算法对图像进行特征增强，然后对图像进行网格划分，针对每个网格的灰度分布情况调整特征点检测时的阈值，之后采取动态区域非极大值抑制方法筛选最佳特征点。实验结果表明，相较于原ORB算法，改进后的算法提取的特征点在图像上的分布更加均匀，当亮度在80%的范围内变化时，特征点的重复率稳定在75%以上，匹配正确率平均提高了22%。

The ORB(oriented FAST and rotated BRIEF) feature detection algorithm is shown to have significant variances in the number of extracted feature points and the accuracy of matching in blurry scenes and environments with drastic lighting changes. Additionally, feature points tend to cluster at the corners of image objects. In response to these issues, an improved ORB feature detection algorithm was proposed. Firstly, the multi-scale retinex(MSR) algorithm was employed to enhance the features of the image. Subsequently, the image was divided into grids, and the threshold for feature point detection was adjusted based on the grayscale distribution of each grid. Finally, a dynamic regional non-maximal suppression method was adopted to filter the optimal feature points. The experimental results indicate that, compared to the original ORB algorithm, the distribution of feature points extracted by the improved algorithm is more uniform across images. The repeatability rate of feature points remains stable at over 75% when the brightness varies within a range of 80%, and the accuracy of matching is on average increased by 22%.