## 2 Related Work  
### Traditional Remote Sensing Detection Methods  
Traditional remote sensing image target detection involves conventional digital image processing and machine learning techniques to identify targets or regions of interest within remote sensing images. Its main processes include preprocessing, feature extraction, target detection, and post-processing [18]. Firstly, the original remote sensing image is preprocessed, including operations such as correction, enhancement, and denoising to improve image quality and accuracy. Next, feature information in the image is extracted by applying traditional image processing algorithms. Commonly used features include color, texture, shape, edges, etc., which can be extracted using techniques such as filtering, segmentation, and edge detection. Then, machine learning methods are utilized for target detection, which can be categorized into classification-based methods and region-based methods. The former utilizes several classifiers like support vector machines or maximum likelihood methods to assign each pixel or region in an image to a specific target class [19]. The difference is that region-based methods extract a set of candidate regions in the image and employ sliding window-based methods or region-growing algorithms to determine and refine these regions [20]. Finally, post-processing techniques such as removing duplicate detections, boundary smoothing, and morphological operations are applied to enhance the accuracy and continuity of the detection results. Throughout the history of traditional target detection algorithms, a common observation is that researchers have faced challenges in designing complex feature expressions due to the lack of effective image characterization. Furthermore, the limited computational power of computers has resulted in slow training speeds for these models [21]. However, in 2001, [PERSON] and [PERSON] introduced the groundbreaking VJ detector [22; 23], significantly improving the detection speed while maintaining high precision. The VJ detector employed sliding windows to exhaustively evaluate each candidate window for potential targets, although this approach was computationally intensive and exceeded the capabilities of computers at the time. Nevertheless, introducing the innovative integral graph computation method in the VJ detector addressed the computational quantity issue. Additionally, a balance between detection accuracy and speed was achieved by utilizing AdaBoost as a feature selector and implementing multi-stage classifiers with a cascade structure. [PERSON] et al. proposed the HOG (Histogram of Oriented Gradients) detector [24]. This detector primarily aims to solve challenges related to feature invariance (e.g., scale, translation, and illumination) and nonlinearity (differentiating between target classes). The HOG detector laid a solid foundation for the development of numerous subsequent detectors, with its techniques being widely applied in various computer vision applications. [PERSON] introduced the Deformable Part-based Model (DPM), which is considered a pinnacle among traditional target detection algorithms. The DPM incorporates a root filter and multiple co-filters, where the model's training process focuses on effectively decomposing the target. In contrast, the prediction process involves aggregating local parts of multiple targets. [PERSON] enhanced the DPM through techniques like Hard Negative Mining, Bounding Box Regression, and Context Priming. These improvements and the \"compiling\" of detection models have played significant roles in boosting detection accuracy and speed. Traditional remote sensing image target detection has both advantages and disadvantages, creating opportunities for the development of deep learning techniques in this field. A key drawback of traditional methods is the reliance on manual feature extraction, which involves subjective selection and the extraction of visual features.