Drones 2025, 9(9), 660; https://doi.org/10.3390/drones9090660 (registering DOI) - 21 Sep 2025
Abstract
In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for
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In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for civil engineering scenarios has heightened the importance of artificially generated content (AIGC) or synthetic data as supplementary inputs. AIGC is typically produced via text-to-image generative models (e.g., Stable Diffusion, DALL-E) guided by user-defined prompts. This study leverages LMMs to interpret key parameters for drone-based image generation (e.g., color, texture, scene composition, photographic style) and applies prompt engineering to systematize these parameters. The resulting LMM-generated prompts were used to synthesize training data for a You Only Look Once version 8 segmentation model (YOLOv8-seg). To address the need for detailed crack-distribution mapping in low-altitude drone-based monitoring, the trained YOLOv8-seg model was evaluated on close-distance crack benchmark datasets. The experimental results confirm that LMM-prompted AIGC is a viable supplement for low-altitude drone crack monitoring, achieving >80% classification accuracy (images with/without cracks) at a confidence threshold of 0.5.
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(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) Applications in Critical Industrial Sectors)
