An automatic detection method for urediniospores of wheat stripe rust based on improved CenterNet model was proposed to solve the serious problems of false detection and missing detection in microscopic images collected by spore capture equipment. Firstly, the feature extraction network in CenterNet was optimized by halving the number of Basic Block layers to improve detection and training speed in view of the characteristics of small spore targets and few species in fungal spore microscopic images. Secondly, according to the characteristic that the spore shape was nearly elliptic or round, the width and height of the target originally used for training was improved into the long and short axis and angle of the target ellipse frame for the training part of CenterNet, which improved the spore segmentation coincidence rate. Finally, the long and short axis mapping rectangle was used to calculate the Gaussian core radius of the elliptical frame heat map to reduce the missed detection rate of spores. The experimental results showed that the identification accuracy of the improved CenterNet detection method for urediniospores of wheat stripe rust was 98.77%, the overlap degree was 83.63%, and the detection speed was 41f/s, which met the application requirements of real-time detection. Compared with the original CenterNet model, the overlap degree was increased by 7.53 percentage points, the detection speed was 11f/s faster, and the model size was reduced by 68.5%. In conclusion, the experimental results indicated that the proposed method can accurately detect and segment fungal spores in the microscopic image, providing technical support for the automatic detection of airborne urediniospores of wheat stripe rust in wheat fields and the early control of wheat stripe rust.