Purpose/Significance To establish an object detection model through mask region-based convolutional neural network(Mask R-CNN), so as to intelligently identify the nodule location in thyroid ultrasound images and provide references for the decision-making of ultrasound doctors.Method/Process 1 650 ultrasound nodule images are collected, and the labelme tool is used to label the nodule locations. The backbone network of Mask R-CNN is replaced by MobileNetV3, ResNet50, ResNet101 and ResNet152 respectively, feature pyramid network and region of interest align are introduced. The model is trained using transfer learning training strategy and the object detection performance is compared under different networks. Result/Conclusion The model trained with ResNet101 for the backbone network has an average accuracy of 86.8%, an average recall rate of 95.3%, and an average F1 score of 90.6%, which is superior to other backbone networks and can detect thyroid nodules more accurately, and has certain clinical application value.