In this study, a detection method for tuberculosis pathogens based on Faster R-CNN is proposed to detect tuberculosis with higher accuracy and lower missed detection rate. First, the Mosaic data enhancement method is used to expand the dataset to improve the generalization ability of the model. At the same time, the K-means clustering algorithm is introduced to re-cluster the used dataset to generate the initial candidate box size of the paired anchor points. Secondly, the original feature extraction network in Faster R-CNN is replaced with Res2Net, and all its convolution kernels are replaced with empty convolution. This can bring a larger receptive field compared with the original convolution when the number of parameters remains unchanged. Furthermore, the improved GC-FPN module is introduced to make the model pay more attention to small target information while being lightweight. Finally, ROI Align is introduced to solve the problem of deviation between the candidate box and the initial regression position. The experimental results show that, compared with the original Faster R-CNN algorithm, the improved Faster R-CNN model has a 2.7% higher accuracy and an 1.4% higher recall rate on the open data set. This algorithm has been verified on the dataset of tuberculosis images and possesses high accuracy.