To address the limitations of existing detection methods in pulmonary nodule detection, such as low accuracy and over-fitting, a pulmonary nodule detection method based on an improved YOLACT model was proposed. In the main structure of the YOLACT model, the original residual network was replaced with DetNet to overcome the limitation of the original model in small nodule detection. Further, a transfer-learning mechanism was introduced in the model training to prevent the over-fitting problem of the original model induced by learning difficulties on a small number of pulmonary nodules, thereby allowing the new model to achieve better detection results. Moreover, the original ReLU function was replaced with the RReLU function to further reduce the possibility of over-fitting. Experimental results on LUNA16 dataset indicate that the proposed method can achieve improvement under several evaluation metrics, such as the working curve of the subject, rate of false positives, rate of missed diagnosis, and accuracy.