# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 1 Introduction  
Remote sensing object detection is a critical task in the field of remote sensing, and its detection performance directly affects the performance of downstream tasks. This task finds significant applications in various civilian and military domains, including search and rescue operations [1; 2], military reconnaissance, and intelligence gathering [3; 4; 5; 6; 7; 8; 9]. Unlike typical object detection tasks, remote sensing object detection faces a unique set of challenges, including variations in scale, differences in viewpoint, changing lighting conditions, background interference, noise interference, low imaging resolution, and significant variations in the morphology and distribution of targets [10; 11]. In addressing these challenges, remote sensing object detection requires the development of algorithms that are robust and adaptive, enabling the effective identification and localization of targets of various scales and types. This may involve the use of multi-scale detection strategies, consideration of variations in target appearance under different viewpoints and lighting conditions, mitigation of the impact of background and noise on detection results, and handling targets in low-resolution images. In summary, remote sensing object detection is a challenging yet highly promising field that demands ongoing research and innovation to address the complexities of various environments and scenarios effectively. Recently, the development of deep learning technologies has continuously broken through the bottlenecks in the field of image processing, achieving groundbreaking progress in areas such as object detection and tracking, image segmentation, image recognition, and retrieval. Natural image datasets like ImageNet [12] and MS COCO [13] have propelled advancements in object detection. However, remote sensing images have unique characteristics where objects often exhibit arbitrary orientations, dense arrangements, and complexbackground information that introduces interference. Therefore, directly applying object detection models designed for relatively simple background images to remote sensing object detection often falls short of practical requirements. All these factors make object detection in remote sensing images, which have complex backgrounds, more challenging. Remote sensing object detection necessitates specially designed algorithms and models to cope with the diversity, density, and complex environmental backgrounds of targets, ensuring efficient and accurate target identification. Research in this field is continuously advancing to meet the practical needs of remote sensing applications. In this research, we focused on enhancing existing natural image object detection frameworks to achieve better performance in satellite remote sensing image object detection tasks. YOLOv8 [14] is the latest object detection framework in the YOLO family, and compared to YOLOv5 [15], it demonstrates the best performance in object detection tasks on natural image datasets such as COCO. However, achieving satisfactory results in satellite image object detection tasks directly with YOLOv8 proves challenging. We updated YOLOv8 primarily in two aspects. Firstly, the neck part of YOLOv8 utilizes the PANet [16] structure. To achieve more effective feature fusion at different scales, this study adopts the BiFPN structure to design a new neck structure for feature fusion. Compared to PANet, BiFPN introduces residual connections to enhance feature representation, removes nodes with a single input edge to reduce computational complexity, and introduces weight fusion. Weight fusion involves adding weights to various scale features being fused, thereby adjusting the contributions of different scales to improve the speed of object detection. Secondly, YOLOv8 uses a Convolutional Neural Network (CNN) to construct its backbone network. CNNs excel at capturing local information, while transformers can enhance the model's global modeling capabilities.