# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 1 Introduction
This stark difference in power requirements poses a significant challenge when it comes to deploying these GPU platforms directly on board. Therefore, it becomes essential to explore alternative approaches or optimize existing algorithms to ensure efficient and effective target detection within the power limitations of small remote sensing satellites. As a result, the conventional approach in satellite remote sensing image processing involves transmitting the captured images from satellites or aerial drones to GPU platforms on the ground for further processing. Due to the power limitations of small remote sensing satellites, it is more practical to offload the computational tasks to powerful GPU platforms that are readily available on the ground. This approach allows for the utilization of high-performance computing resources while overcoming the power constraints of the satellite. By transmitting the images to a GPU platform on the ground, researchers can leverage the capabilities of these platforms to process the remote sensing data efficiently and accurately. However, the process of downloading image data to the ground for processing introduces significant delays in data processing, resulting in reduced system efficiency. This limitation poses challenges in meeting real-time detection requirements [15]. To address this issue, researchers have begun exploring the deployment of convolutional neural network (CNN) models on application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs) [16]. These specialized hardware platforms offer the potential for accelerating and optimizing the processing of image data, thereby improving overall system efficiency. By leveraging the parallel computing capabilities of ASICs and FPGAs, researchers aim to overcome the latency associated with transmitting data to the ground, enabling more efficient real-time detection. This research direction can overcome the challenges posed by traditional GPU processing and meet the stringent requirements of real-time detection in remote sensing applications. Among them, FPGAs have emerged as an alternative to GPUs in spaceborne scenarios due to their advantages such as low power consumption, high performance, parallel computing capabilities, programmability, and customization. They possess the characteristics of high flexibility and lower cost compared to ASICs [17]. When deploying CNN models on embedded devices, it is common to trade off some accuracy in order to improve detection speed and reduce the number of model parameters. Currently, commonly used methods include model compression [18] and the utilization of more lightweight network models [19] to decrease the model size. These techniques enable the deployment of CNN models on embedded devices with reduced computational complexity and memory footprint, while still maintaining acceptable performance levels. To cater to the real-time target detection needs of remote sensing images in spaceborne applications, while addressing challenges related to deep learning model parameters, computational complexity, and hardware platform deployment, this paper presents an efficient lightweight algorithm called YOLOv4-MobileNetv3. Additionally, channel pruning is performed on this algorithm. Furthermore, the Xilinx Vitis AI tool chain is employed to quantize, compile, and deploy the pruned network model at the edge. The primary contributions of this paper can be summarized as follows:  
* Significant Reduction in Model Parameters: The acceleration strategy we proposed achieves a remarkable reduction in the number of model parameters, with the parameter size being only 0.09 times that of the original YOLOv4. This reduction not only enhances the efficiency of model storage but also contributes to faster inference times. * Proposed Deployment Scheme: We have developed a novel deployment scheme for YOLOv4-MobileNetv3 on an FPGA using channel layer pruning.