Compared to conventional deep neural networks (DNNs), spiking neural networks (SNNs) have gained significant attention in recent years owing to their low power consumption, superior inference efficiency, and high biological plausibility. Although SNNs have demonstrated success in simple classification tasks, they are not frequently employed in complex regression tasks such as object detection. During the inference process, the multi-time-step nature of SNNs can lead to multiple storage accesses and computational operations, thereby increasing the power consumption and inference latency. In this study, we introduce Spiking-HybridYOLO, a hybrid architecture designed for low-time-step object detection. Distinct from the conventional DNN-to-SNN conversion approaches, Spiking-Hybrid-YOLO can be trained directly using surrogate gradient methods. After examining the computational complexity of each layer in YOLO, we selectively replace certain ANN layers with spiking layers to strike a balance between execution time, energy consumption, and accuracy. Experimental results show that Spiking-HybridYOLO achieves low-power object detection in a single timestep, consuming only 2.77% of the power used by ANNs.