# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 5 Conclusions  
This paper proposes an FPGA-based image processing solution for remote sensing images on the aerospace platform, which is based on a lightweight network and network pruning technique applied to YOLOv4-Mobilenetv3. The main improvements of this solution include the use of the lightweight feature extraction network MobileNetv3, aiming to reduce the model parameters and improve the detection speed. To further reduce the model parameters and improve computational efficiency, deep separable convolutions are utilized. Additionally, channel pruning techniques are combined to prune redundant parameters, further reducing the model parameters and redundant calculations, thereby improving computational efficiency. In response to the decrease in accuracy after pruning, we fine-tune the model to enhance its accuracy, even surpassing the accuracy before pruning. To adapt to the complex backgrounds of remote sensing images, we apply data augmentation \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**FPGA**} & \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Power (W)**} \\  & **Evaluation** & & & & \\  & **Board** & & & \\ \hline
[53] & ZCU104 & YOLOv4 & 27.97 & 15.82 \\
[54] & ZCU104 & YOLOv4 & 17 & 20 \\ Ours & KV260 & YOLOv4 & 48.14 & 7.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different studies. Figure 12: Before and after quantization, different effects were compared. (**a**) Before quantization; (**b**) after quantization. techniques such as Mosaic and GridMask on the DIOR dataset to enhance the model's robustness and adaptability to various complex backgrounds. For the UltraScale + MPSoC platform used, we utilize the matching quantization tool Vitis AI Quantizer to perform post-training quantization (PTQ) on the pruned YOLOv4-Mobilenetv3 network model, and compile it using Vitis AI compiler, finally deploying it on the Xilinx KV260. The comparative experimental results on the DIOR dataset demonstrate that our improved network model outperforms mainstream object detection models in terms of mAP and parameter quantity, with mAP slightly lower than the original YOLOv4 model. To validate the superiority of our FPGA acceleration solution, we select the mainstream deep learning hardware deployment platforms, CPU and GPU, for performance comparison. Experimental results show that our FPGA deployment solution has significant advantages in terms of power consumption and FPS. Compared with CPU AMD R7 4800H, FPS improves by 317.88% and power consumption decreases by 81.91%; compared with GPU NVIDIA RTX 2060 (6G), power consumption decreases by 91.85%, and FPS increases by 8.50%. In the scenarios of unmanned aerial and aerospace remote sensing image processing, limited by power consumption, weight, and volume, our design solution meets the requirements of low power consumption and high real-time performance. The high-performance, low-power image processing hardware acceleration solution proposed in this paper provides some ideas for deploying CNN models on satellite platforms and unmanned aerial platforms. There are still areas for improvement in our experimental approach, as this paper mainly focuses on the post-processing of remote sensing images and only provides a data augmentation scheme for pre-processing. However, in practical engineering, there are usually issues such as noise, distortion, or uneven lighting in the original images captured by satellites. Therefore, pre-processing operations are required before object detection and recognition. Pre-processing steps may include denoising, geometric correction, radiometric calibration, etc., to improve image quality and accuracy. Furthermore, satellite images often have large scales and high resolutions, requiring operations such as cropping, scaling, or segmentation to adapt to specific application scenarios or analysis needs.