A Synthetic Data Generation Approach With Dynamic Camera Poses for Long-Range Object Detection in AI Applications

Accurate long-range object detection is essential for applications such as security and surveillance. However, existing datasets often lack the complexity needed to represent real-world outdoor environments, resulting in limited performance of object detection algorithms at extended distances.Synthetic data generation offers a way to address these limitations by creating varied and realistic training scenarios. To address these limitations, we propose a novel approach utilizing BlenderProc procedural generation and photorealistic rendering to create a synthetic dataset that captures diverse and realistic outdoor scenes for the objects detection. We trained YOLO model on this dataset and compared its performance with standard YOLO model. Our approach achieved a precision of 94% and recall of 96% for detecting objects at distances exceeding 120 meters, demonstrating significant improvements over existing methods. These findings underscore the potential of advanced synthetic data generation techniques to enhance long-range object detection and address critical challenges in surveillance, remote sensing, and autonomous systems.