# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 1 Introduction  
In recent years, UAVs have become extensively utilized across military, commercial, and everyday sectors due to their small size, affordability, and high practicality. Among these applications, target detection from a UAV perspective has emerged as a critical research direction. Target detection primarily relies on high-resolution sensors (e.g., cameras, infrared cameras) mounted on UAVs to capture large volumes of image data, which are then automatically detected and recognized using computer vision and machine learning techniques [1, 2]. Target detection methodologies can be broadly classified into two distinct categories: conventional image processing techniques and approaches leveraging deep learning frameworks. Conventional methods, such as frame difference and background subtraction, leverage the dynamic changes in moving targets relative to the background for detection. These methods offer advantages such as low computational complexity, fast processing speed, and simplicity. However, their accuracy and real-time performance are often insufficient to meet practical requirements. In contrast, deep learning-based methods autonomously learn useful information from image and video datasets throughfeature extraction, followed by target classification and recognition, offering higher accuracy and stronger generalization capabilities [3]. Deep learning-based target detection algorithms are broadly divided into two categories based on the detection stages: two-stage and one-stage methods. Two-stage methods, represented by algorithms such as R-CNN [4], Fast R-CNN [5], and Faster R-CNN [6], first generate candidate regions that may contain targets and then classify and regress bounding boxes using deep learning models to improve detection accuracy. However, these methods typically require substantial computational resources, resulting in slower processing speeds, which limits their suitability for real-time applications. On the other hand, one-stage methods integrate the target detection task directly into a single neural network, eliminating the need for candidate region generation and directly outputting target categories and location information. These methods exhibit faster inference speeds and stronger real-time performance. Representative one-stage algorithms include YOLO [7,8], SSD [9], and RetinaNet [10]. Compared to one-stage detection algorithms such as SSD and RetinaNet, the YOLO algorithm treats target detection as a regression problem, utilizing a single neural network for prediction. Its concise architecture offers faster inference speeds and lower requirements for hardware resources and computational power. It has undergone significant evolution since its initial introduction, with each iteration introducing improvements in accuracy, speed, and adaptability to various detection tasks. These advancements make it particularly suitable for real-time applications, including UAV-based target detection, where efficiency and accuracy are critical. However, in the domain of UAV-based target detection, the accurate identification of small targets, characterized by their limited pixel coverage and reduced feature discriminability, has emerged as a critical area of investigation in contemporary studies. Therefore, based on the use of the eighth generation of YOLO algorithm, this study proposes DGBL-YOLOv8s, an improved object detection model tailored for UAV perspectives based on YOLOv8s. The main contributions of this study are as follows:  
1. Based on the Dilation-wise Residual (DWR) module from the DWRSeg network [11], a C2f-DWR module is designed to enhance multi-scale contextual information acquisition. This module enhances the C2f module's capacity to fuse multi-level feature maps, allowing the model to extract finer target details and richer contextual information. 2. A Global-to-Local Spatial Aggregation Bidirectional Feature Pyramid Network (GLSA-BiFPN) is designed for the neck structure.