Objective : The unintentional retention of needles in patients can lead to severe consequences. To enhance acupuncture safety, the study aimed to develop a deep learning-based cloud system for automated process of counting acupuncture needles. Methods: This project adopted transfer learning from a pre-trained Oriented Region-based Convolutional Neural Network (Oriented R-CNN) model to develop a detection algorithm that can automatically count the number of acupuncture needles in a camera picture. A training set with 590 pictures and a validation set with 1 025 pictures were accumulated for finetuning. Then, we deployed the MMRotate toolbox in a Google Colab environment with a NVIDIA Tesla T4 Graphics processing unit (GPU) to carry out the training task. Furthermore, we integrated the model with a newly-developed Telegram bot interface to determine the accuracy, precision, and recall of the needling counting system. The end-to-end inference timewas also recorded to determine the speed of our cloud service system. Result: In a 20-needle scenario, our Oriented R-CNN detection model has achieved an accuracy of 96.49%, precision of 99.98%, and recall of 99.84%, with an average end-to-end inference time of 1.535 s. Conclusion The speed, accuracy, and reliability advancements of this cloud service system innovation have demonstrated its potential of using object detection technique to improve acupuncture practice based on deep learning.