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%.