Abstract: Pattern recognition methods of partial discharge (PD) utilizing images are efficient for the single PD source, yet they face challenges in recognizing the multi-source PD. An object detection model is proposed for the recognition of multi-source PD according to Deformable detection with transformers (Deformable DETR). Typical single-source PD and multi-source PD signals are collected by experiment. Two types of PD spectra, namely phase-resolved partial discharge spectrum and polar coordinate phase-resolved spectrum, are used to generate the data set. The denoising training task and Bayesian optimization algorithm are introduced to optimize the performance of the Deformable DETR model. Single-source and multi-source PD spectra are identified by the optimized PD Deformable DETR model. Results show that the proposed model can effectively recognize the source of single- and multi-PD patterns. In addition, compared with common types of object detection models, the performance of the PD Deformable DETR model can be evidently improved at the cost of losing a few efficiencies. Finally, the PD spectra of real motors with insulation defects are identified by the PD Deformable DETR model. The recognition accuracy reaches 91%, which shows the validity of this proposed method. Additionally, the acquisition and recognition program of PD spectrum is developed. The paper provides novel perspectives for identifying multi-source PD.