Abstract
Human body detection is critical in post-disaster response, especially in UAV-assisted search and rescue missions, where accurate localization of stranded individuals is essential for efficient emergency operations. However, current disaster scene image datasets are often scarce, poorly annotated, and of inconsistent quality, limiting the performance of deep learning models in complex disaster environments (e.g., ruins, fires, floods). These challenges are exacerbated when human targets are intertwined with complex backgrounds, making existing techniques inadequate for practical needs. To address these limitations, this paper presents an integrated approach combining image generation and target detection techniques to enhance the adaptability and accuracy of deep learning models. By constructing diverse, high-quality disaster scene datasets, the proposed method alleviates data scarcity and improves generalization under complex scenarios. Models trained on the expanded dataset achieve significant improvement, with generated images yielding an FID score of 45 and mAP50 scores exceeding 98% across three detection models. Additionally, this study explores the integration of computer vision and human-computer interaction in UAV search and rescue missions. The proposed framework provides an intelligent, efficient solution for post-disaster search and rescue, demonstrating the transformative potential of data augmentation and target detection technologies in disaster management. This work establishes a robust technological foundation for future post-disaster response and intelligent search and rescue operations.
