Evaluation of Multiple Optimized AIGC Approaches in Low-Altitude Drone-based Riparian Waste Monitoring

Abstract

Recently, LMM (large multimodal model, which can“understand”image, text or other types of modals) has been applied in camera-based riparian environmental monitoring (4K camera or drones). Concurrently, LMM can also assist Image-based Generative AI to generate AIGC (Artificial Intelligence Generated Content) using prompt engineering (i.e., users can drive the computers to provide the prompts for image generation purposely during the conversation with LMM). In this research, the authors designed multiple experiments to apply LMMs (i.e., Tencent YuanBao-interfaced DeepSeek and Hunyuan) to generate the prompts using prompt engineering. Based on the different prompt outputs, AIGC-based waste images were generated in Stable Diffusion. Correspondingly, generated images were trained in YOLOv8x models with the assistance of the annotations derived from previous trained AIGC waste model. Based on the needs of low-altitude waste monitoring, the authors tested the trained YOLOv8x models using benchmark dataset (LAD-RWB, Low-Altitude Drone Riparian Waste Benchmark). In conclusion, LMM-derived prompt-based AIGC have the possibility of applying in the low-altitude drone waste monitoring with over 0.8 precision (0.426 recall and 0.491 mAP50) in detecting the waste under the riparian background using a tiny test dataset (16 images). LMM can provide reasonable prompts for Stable Diffusion image generations.