Manual screening of trigeminal nerves requires high professional quality and is time consuming for clinicians. Using deep learning to automatically detect trigeminal nerve regions in cranial Magnetic Resonance Imaging (MRI) can provide a reliable input image for subsequent trigeminal nerve segmentation. YOLO(You Only Look Once) network is utilized to automatically detect the trigeminal nerve region of the cranial magnetic resonance image to improve the inference speed, and to systematically evaluate the inference performance of the NVIDIA TensorRT framework under different computing platforms. The experimental results show that the YOLO target detection network can accurately detect the area where the trigeminal nerve is located. Simultaneously, under the NVIDIA TensorRT framework, when the input brain MRI resolution is (204×204), the YOLOv2 network detects the optimized trigeminal nerve through the CPU platform, embedded GPU platform, desktop GPU platform and professional GPU computing card platform, the frame rates per second can reach 0.1 FPS, 23.4 FPS, and 793.7 FPS . This provides important reference for the subsequent development of portable trigeminal neural segmentation equipment.