# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 1 Introduction  
Remote sensing image target detection has been extensively applied in various fields, including land resources planning [1], glacier change monitoring [2], disaster prevention and relief [3], military national defense [4], urban safety supervision [5], forestry vegetation monitoring [6], and many others. It holds significant scientific research value and offers broad application prospects. Remote sensing image object detection can be broadly categorized into traditional human-based feature extraction methods and deep learning-based methods [7]. Traditional methods involve manually designing effective feature extractors to perform target detection. On the other hand, deep learning methods employ convolutional neural networks (CNNs) to extract features in a data-driven manner, allowing for continuous learning and achieving object detection tasks. With the continuous advancement and maturation of deep learning theory and technology, object detection performance based on deep learning has surpassed traditional methods by a significant margin. It has also gained broader applications in various domains, including image classification and object detection tasks. Deep learning-based general object detection algorithms include anchor-based methods. Prominent examples of anchor-based algorithms are one-stage detection algorithms like SSD [8]; the YOLO series [9]; and two-stage detection methods like Faster R-CNN [10] and Mask R-CNN [11]. Each of these algorithms has its own strengths and offers distinct advantages in practical engineering applications. The two-stage detection algorithm divides the object detection process into two stages. First, it generatescandidate regions and then refines their positions before classifying them. The advantages of the two-stage detection algorithm are its low detection and recognition error rates, as well as a low rate of false positive detections. However, it has the drawback of slower detection speed, making it less suitable for real-time object detection tasks in videos or images. Therefore, two-stage detection algorithms are commonly used in applications that require high-precision object detection, such as face recognition, medical image analysis, and so on. In contrast, the one-stage detection algorithm does not involve a separate stage for generating candidate regions. Instead, it directly generates the category probability and position coordinate values of the objects. The final detection result can be obtained after a single pass, resulting in faster detection compared to the two-stage algorithm. Therefore, single-stage detection algorithms are commonly used in scenarios that require fast processing, such as real-time video monitoring, autonomous driving, and so on. In recent years, there have been significant advancements in anchor-free object detection algorithms, such as CenterNet [12] and FCO (fully convolutional one-stage object detection) [13]. These algorithms revolutionize the traditional approach by eliminating the dependence on predefined anchors. Target detection in remote sensing images has garnered significant attention in the field. The advancement of remote sensing satellite technology in recent years has spurred research in spaceborne remote sensing image target detection systems. When applying target detection in spaceborne remote sensing images to practical engineering, it becomes crucial to consider not only detection accuracy but also real-time capabilities of the network model and power consumption of the deployment platform. These factors play a vital role in ensuring efficient and effective target detection in the context of remote sensing imagery. To illustrate, let us consider the power constraints of small remote sensing satellites like CubeSats. These satellites typically operate within a power budget of only 2-8 W [14]. In contrast, GPU platforms such as A100, RTX 2080 Ti, RTX 3090, Tesla V100, and others have power consumption levels that far exceed the carrying capacity of the satellite.