Orbital remote sensing platforms increasingly rely on CNN-based object detection for real-time situational awareness. However, deploying these models on spaceborne edge devices is challenging because of stringent Size, Weight, and Power (SWaP) constraints. In addition, the branch-and-merge topology of conventional single-stage detectors increases on-chip memory usage and introduces pipeline stalls, limiting efficient FPGA implementation. To address these challenges, we proposed RS-YOLO, an object detection algorithm developed through a hardware–software co-design approach. Structural re-parameterization converts heterogeneous branches into a sequential stream of padding-free convolutions, producing a deterministic dataflow and reducing per-state combinational control complexity and data-path multiplexing overhead. To mitigate the high-entropy concentration at the center of the re-parameterized kernels, we further introduce a spatial heterogeneous quantization (SHQ) engine. The SHQ engine assigns 16-bit precision to the central coefficients while preserving vectorized 8-bit computation for peripheral elements, reducing quantization errors for small targets with minimal hardware overhead. Experimental results on the Xilinx Zynq-7020 platform show that the proposed system consumes only 2.24 W while achieving a mean Average Precision (mAP) of 0.887 on the NWPU VHR-10 dataset, representing a 1.4% decrease compared with the FP32 baseline. The system also achieves an energy efficiency of 15.19 GOPS/W, demonstrating an effective balance between hardware efficiency and detection performance for resource-constrained edge platforms such as micro-satellite payloads.