# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 5 Discussion  
The experimental results indicate that the DGBL-YOLOv8s model has accomplished significant improvements in scenarios captured by drones, particularly in detecting targets with small scales, large quantities, and long distances. However, despite the model's impressive detection accuracy and performance enhancements, there are still some issues worthy of in-depth discussion. Firstly, the model exhibits certain limitations in detecting high-density target areas or extremely small objects. Especially in scenarios with intricate backgrounds or overlapping multiple targets, the improved model may experience some interference, leading to a decline in detection accuracy for small objects. Secondly, although this paper introduces the GLSA-BiFPN structure in the neck part and the concept of lightweight shared convolution and group normalization in the detection head to shorten the model's computational complexity, the data in Table 6 reveal that the addition of a p2 layer small-target detection head significantly increases the model's complexity. Consequently, deploying the model on devices with limited computational resources could impose a substantial computational burden. The computational complexity (GFLOPs) of the model after incorporating the improvements is shown in Table 6.  
Figure 12: Comparison of missed detection: (**a**) unimproved detection results and (**b**) improved detection results.  
Figure 13: mAP Comparison between different model categories.  
I: The baseline YOLOv8s model has a computational complexity of 28.5 GFLOPs.  
II: The introduction of the DWR module reduces the computational cost slightly to 28.1 GFLOPs. This indicates that it enhances feature extraction and contextual information capture without significantly increasing computational complexity.  
III: The GLSA-BiFPN module further reduces the computational cost to 26.7 GFLOPs. This demonstrates that its structure not only improves multi-scale feature fusion, but also optimizes computational efficiency.  
IV: Due to the introduction of additional detection layers and new mechanisms in LSCDH to improve the detection accuracy of small objects, its computational overhead increases to 45.7 GFLOPs.  
V: The final proposed model, DGBL-YOLOv8s, has a computational cost of 53.7 GFLOPs. This increase is primarily attributed to the combination of the DWR module, GLSA-BiFPN, and LSCDH. Despite the higher computational cost compared to the baseline, the DGBL-YOLOv8s model achieves significant improvements in detection accuracy (8.5% increase in mAP) and model efficiency (35.1% reduction in parameters and 33.9% decrease in model size).  
Consequently, future research should focus on incorporating advanced model compression methods, including pruning, quantization, knowledge distillation, and lightweight architecture design, to minimize the model's parameters and computational demands. This would facilitate its deployment on resource-constrained devices, such as drones and mobile platforms. Additionally, to address the challenges posed by complex drone-captured scenarios, it is worth considering the integration of reinforcement learning with meta-learning or online learning techniques. This would enable the model to autonomously adjust its parameters and strategies in dynamic environments, enhancing real-time performance and robustness. Furthermore, exploring multi-modal data fusion, such as combining visible light, infrared, and LiDAR data, could significantly improve detection performance in complex scenarios. Future studies should also prioritize small object detection by investigating higher-resolution inputs, refined feature extraction methods, and specialized loss functions. Finally, developing larger and more diverse datasets, along with comprehensive evaluation metrics, would provide a stronger foundation for validating model generalization capabilities in real-world applications.