Waste pollution detection has emerged as one of the crucial environmental concerns in recent years, and the accuracy of this practical application has been significantly improving with advancements in deep learning (DL) algorithms. To efficiently detect and quantify waste over large areas, the use of unmanned aerial vehicles (UAVs) has become essential. However, UAV flights and real-world image collection pose challenges that demand expertise, significant time, and financial investments. These challenges are particularly prominent in specialized applications such as waste detection, which rely on large amounts of data. Notably, the availability of adequate and accurately labeled data is vital for the performance of object detection models. Therefore, the identification and acquisition of suitable training data are critical objectives of this study. While ensuring data quality, AI-Generated Content (AIGC), specifically derived from Stable Diffusion, is emerging as a promising data source for DL-based object detection models. This research employed the Stable Diffusion to generate images by utilizing the prompts generated from specified images. Subsequently, the public dataset-based existing trained model automatically labeled the AIGC, which were then assigned corresponding labels in a uniform ratio for training, validation, and testing purposes. To assess the performance differences between the generated dataset and the dataset collected from real-world scenarios, several benchmark datasets were used for accuracy evaluation in this work. The results revealed that the AIGC exhibited superior accuracy in identifying high Ground Sample Distance (GSD) targets in simple backgrounds compared to the realistic collected dataset (F1 score-based). The results demonstrate the potential of AIGC in providing data for object detection models.
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