Small-object detection in satellite remote sensing images plays a pivotal role in the field of remote sensing. Achieving high-performance real-time detection demands not only efficient algorithms but also low-power, high-performance hardware platforms. However, most mainstream target detection methods currently rely on graphics processing units (GPUs) for acceleration, and the high power consumption of GPUs limits their use in resource-constrained platforms such as small satellites. Moreover, small-object detection faces multiple challenges: the targets occupy only a small number of pixels in the image, the background is often complex with significant noise interference, and existing detection models typically exhibit low accuracy when dealing with small targets. In addition, the large number of parameters in these models makes direct deployment on embedded devices difficult. To address these issues, we propose a hybrid overlapping acceleration architecture based on FPGA, along with a lightweight model derived from YOLOv5s that is specifically designed to enhance the detection of small objects in remote sensing images. This model incorporates a lightweight GhostBottleneckV2 module, significantly reducing both model parameters and computational complexity. Experimental results on the TIFAD thermal infrared small-object dataset show that our approach achieves an average precision (mAP) of 67.8% while consuming an average power of only 2.8 W. The robustness of the proposed model is verified by the HRSID dataset. Combining real-time performance with high energy efficiency, this architecture is particularly well suited for on-board remote sensing image processing systems, where reliable and efficient small-object detection is paramount.