Accurate automatic detection of typhoon eye position can provide a priori information for typhoon forecast and monitoring research to reduce disaster loss. Due to the variability of typhoon morphology, it is still difficult to locate typhoon center automatically. In this paper, a R-CNN method for typhoon eye detection based on multi-scale mosaic is proposed with typhoon satellite cloud images. More than 5000 typhoon satellite cloud images released by Japan Meteorological Agency from 1981 to 2017 are collected. The typhoon eye in the image based on the contour curves of the eye wall and the clear brightness difference between the inside and outside of the typhoon eye is segmented. The original image is divided into multi-scale typhoon cloud images by multi-scale estimation algorithm of typhoon eye radius, and the training set and test set are integrated. With the help of multi-scale image mosaic, hyperparameter selection and multi-condition test analysis, the overall algorithm framework of detecting and segmental typhoon eye using multi-scale Mask R-CNN model is constructed, and multi-scale comparison experiments are carried out. In the self-built calibration dataset, the identification accuracy of typhoon eye is from 88.36% up to 92.63%. The average detection time of each image is at least 0.043 s, the minimum mean square error is 2154,and the maximum average crossover ratio is 0.9454. The experimental results show that the proposed multi-scale mosaic data augmentation method has the best effect in large and medium scale scale fusion, but is poor in small and medium scale fusion. Compared with the existing main data augmentation methods, it can improve the accuracy of neural network more effectively. The comprehensive efficiency of the whole detection model in typhoon center location is better than other deep learning localization methods.