Optimization of YOLOv8 for UAV-Based Object Detection: A Literature Review

YOLOv8 offers high accuracy and real-time performance, making it suitable for UAV applications. However, challenges such as limited onboard computing power, varying target sizes, and complex environments persist. This review systematically explores recent advancements in optimising YOLOv8 for drone applications, while also studying and evaluating its performance under extreme conditions such as low light and occlusion. The results indicate that current optimisations primarily focus on three directions. First, improving small object detection accuracy through multi-scale feature fusion, attention mechanisms (such as hybrid attention modules), and data augmentation (such as adaptive anchor allocation). Second, achieving model lightweighting and edge deployment through backbone replacement (such as MobileNetV3), model quantisation, and embedded deployment optimisation. Additionally, robustness in complex environments is improved through techniques such as image preprocessing (e.g., CLAHE, gamma correction), adversarial training, and multimodal fusion..This work aims to guide future studies toward automated optimization, edge-cloud integration, and multi-task learning for intelligent UAV vision systems.