# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 2 Related Work  
### Object Detection  
The field of object detection has undergone over two decades of development, with its task being to identify objects of interest in images or videos and accurately determine their positions and sizes [17]. Unlike image classification tasks, object detection not only involves object classification but also requires precise localization of objects within images. The development of object detection can be divided into two main periods: the era of traditional object detection algorithms (1998-2014) and the era of deep learning-based object detection algorithms (2014-present). During the era of traditional object detection algorithms, object detection heavily relied on traditional computer vision and image processing methods. Some classic algorithms included methods based on feature engineering, such as Haar feature cascades [9] and Histogram of Oriented Gradient (HOG) features [18], among others. While these methods performed well in some scenarios, they had limitations when dealing with challenges like multi-scale objects, complex backgrounds, and occlusions. In the era of deep learning-based object detection algorithms (2014-present) [19; 20; 21; 22; 23], with the rise of deep learning technology, significant breakthroughs were achieved in object detection. Deep learning demonstrated clear advantages in feature learning and representation, enabling more effective handling of complex object detection tasks. Traditional object detection algorithms gradually gave way to deep learning-based methods. Within deep learning-based object detection, two main technical approaches emerged: anchor-based methods (one-stage, two-stage) and anchor-free methods. In summary, the field of object detection has made remarkable progress, and the application of deep learning technology has provided new opportunities and methods for addressing various complex object detection tasks. This field continues to evolve to meet the demands of different application domains. In anchor-based object detection, common two-stage algorithms like Faster R-CNN [24] and Cascade R-CNN [25] are utilized. These algorithms are typically divided into the following two stages: generating region proposals and object detection. In stage 1, the algorithm initially generates a set of candidate regions or bounding boxes from the input image. These regions may potentially contain objects of interest. This step is often accomplished through a Region Proposal Network (RPN), which effectively extracts regions from the image that might contain objects of interest and provides them for further processing in the next stage. In stage 2, using the candidate regions generated in stage 1, the algorithm performs actual object detection. This involves classifying each candidate region to determine whether it contains an object, as well as regressing the position and boundaries of the object if present. Typically, this stage involves convolutional neural networks (CNNs) along with classification and regression heads to perform these tasks. One-stage object detection methods differ from traditional two-stage approaches in that they eliminate the need for the region proposal stage. Instead, they directly generate the object's class probabilities and positional coordinates. Through a single stage of processing, these algorithms can swiftly obtain the final detection results, resulting in faster detection speeds. Some well-known one-stage object detection algorithms include Yolov1-5 and RetinaNet [26]. The development of these algorithms represents the continuous progress of the object detection field towards greater efficiency and practicality. The anchor-free object detection algorithms discard the excessive use of anchors found in traditional anchor-based detection methods, thus reducing computational complexity and the number of hyper-parameters in the models. This contributes to improving model performance.