Automated UAV Object Detector Design Using Large Language Model-Guided Architecture Search

Neural Architecture Search (NAS) is critical for developing efficient and robust perception models for UAV and drone-based applications, where real-time small object detection and computational constraints are major challenges. Existing NAS methods, including recent approaches leveraging large language models (LLMs), often suffer from static resource allocation and ambiguous architecture generation, limiting their effectiveness in dynamic aerial scenarios. In this study, we propose PhaseNAS, an adaptive LLM-driven NAS framework designed for drone perception tasks. PhaseNAS dynamically adjusts LLM capacity across exploration and refinement phases, and introduces a structured template language to bridge natural language prompts with executable model code. We also develop a zero-shot detection score for rapid screening of candidate YOLO-based architectures without full training. Experiments on NAS-Bench-Macro, CIFAR-10/100, COCO, and VisDrone2019 demonstrate that PhaseNAS consistently discovers superior architectures, reducing search time by up to 86% while improving accuracy and resource efficiency. On UAV detection benchmarks, PhaseNAS yields YOLOv8 variants with higher mAP and reduced computational cost, highlighting its suitability for real-time onboard deployment. These results indicate that PhaseNAS offers a practical and generalizable solution for autonomous AI model design in next-generation UAV systems.