The current object detection algorithm can detect objects accurately but fails to determine the riskiest object among all obstacles. The lack of prioritizing obstacles according to risk leads to less efficient decisions made in the indoor setting. This paper proposes RAOP (Risk-Aware Obstacle Prioritization); an enhancement of the state-of-the-art detection framework, YOLOv11, by adding a post-detection Risk Assessment engine based on distance between obstacles, object size, semantics of the obstacles, and object detection confidence. To enhance the performance of the system under challenging indoor lighting conditions, CLAHE is added as a preprocessing step. The proposed framework categorizes risk levels to four actions: STOP, SLOW DOWN, AVOID, and SAFE. Performance results show that the proposed method outperforms its counterparts, providing improvements of 6.49%, 0.81%, and 16.25% in precision, mAP@50, and low-light object detection accuracy, respectively. Finally, a GUI-based standalone software implementation verifies its effectiveness for indoor robotic navigation tasks.