UPHSID: Underwater PET–HDPE Synthetic Image Dataset
Abstract
Dataset Description This dataset contains a collection of synthetic underwater images of PET and HDPE plastic debris, generated using two state-of-the-art generative AI models: Stable Diffusion XL (SDXL) and DALL·E-3. The dataset was developed to address the scarcity of balanced, high-quality underwater imagery for deep learning models used in marine plastic detection. All images were generated using a structured, multi-stage prompt engineering workflow described in the accompanying publication titled "Synthetic Data for PET and HDPE Classification: A Comparison of Diffusion and Autoregressive Models" at 33rd International Conference on Artificial Intelligence and Cognitive Science (AICS 2025). The image generation design was driven by prompt engineering, which followed a structured, iterative process (prompts P0-P6). The steps builds from basic objectdefinitions (P0) with progressivley scaling to include environmental and object specific features, such as environmental context (P1), optical/physical parameters (P2), material-specific tokens (P3), biotic interactions (P4), and spectral cues (P5). An optional final composite stage (P6) combined these elements into physics-compliant prompts that integrated materials, flora/fauna, and spectral descriptors. Negative prompts were systematically applied after P3–P5 to discard artefacts (e.g., logos, watermarks, non-plastic materials, and unrealistic lighting). Contents The dataset includes 200 synthetic underwater images: 100 PET…
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