Background: Rapid differentiation between ischemic and hemorrhagic stroke is critical for timely treatment, yet diffusion-weighted imaging (DWI) alone poses diagnostic challenges for hemorrhage detection. Artificial intelligence (AI) offers potential to improve radiologist interpretation, but comparative evaluations of state of-the-art object detection models on stroke MRI remain limited. Objective: To evaluate and compare the performance of YOLOv8 and Faster R-CNN for automated detection of intracranial hemorrhage and acute infarction on DWI, benchmarked against expert neuroradiologists. Methods: In this retrospective single center study, 1,000 adult DWI cases were analyzed, comprising 334 hemorrhage, 333 infarct, and 333 normal studies. Images were annotated by neuroradiologists, and models were trained with and without augmentation. Performance was assessed at lesion and image levels using precision, recall, mean average precision (map), confusion matrices, and inference time. Binary hemorrhage detection was compared with radiologists using McNemar’s test. Results: YOLOv8 achieved higher recall and map than Faster R-CNN, particularly for small infarcts and subtle hemorrhages. With augmentation, recall improved to 0.886 and mAP@0.5 reached 0.903. Binary hemorrhage detection yielded sensitivity 0.91, specificity 0.88, and accuracy 0.90. Radiologists achieved near-perfect accuracy of 0.99, while Faster R-CNN lagged with sensitivity 0.82. YOLOv8 processed each image in 40 MS for Faster R-CNN. Conclusion: YOLOv8 demonstrated superior accuracy and efficiency compared with Faster R-CNN, approaching radiologist-level sensitivity. These findings support the potential of one-stage detectors to augment radiologists in real-time stroke workflows, warranting further multicenter and multi-sequence validation.