UAV image object detection based on improved YOLOv8

In a variety of UAV application scenarios, such as agricultural monitoring, urban safety and traffic management, high-precision image object detection is crucial for real-time analysis and decision-making. Traditional object detection algorithms are often ineffective in dealing with small objects in complex environments. To this end, this paper proposes an improved YOLOv8 model that integrates a NAM (Normalization-based Attention Module) attention mechanism and a Bidirectional Feature Pyramid Network (BiFPN) specifically to optimize the object detection performance of UAV images. The NAM module enhances the model's spatial attention capability by utilizing a normalization process that This enables the model to adaptively emphasize important feature regions in the image, which significantly improves the recognition accuracy of small-scale objects without adding too much computational burden. BiFPN further optimizes the flow of information between different scales, which improves the efficiency of feature utilization and the overall performance of detection. Extensive experiments on the VisDrone2019 dataset show that the improved YOLOv8 model improves the mAP metrics by 11.3% compared to the original model, and performs especially well in scenes with complex backgrounds and the presence of multi-scale objects. This study not only validates the potential application of NAM in UAV vision tasks, but also provides a new technical path for the implementation of deep learning models in real monitoring systems.