An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys

Abstract

Deep-learning-based object detection in UAS imagery is crucial for outfall surveys and basin environmental protection. Comprehensive UAS image datasets serve as a foundation for creating deep-learning-based outfall-detection models and evaluating outfall-detection algorithms. However, existing remote sensing deep learning datasets lack specific outfall data. This study introduces a benchmark UAS image dataset of outfalls, categorized into three main and seven subcategories. Over 10,000 labeled images were collected from UAS images with a 10 cm resolution for the Yangtze River, Yellow River, and other basins from 2019 to 2022, covering nearly all types of outfalls in China. Each sample was matched with digital surface model (DSM) data generated through photographometry or the DSM transfer method. Evaluation with seven widely used deep learning-based object detection algorithms demonstrated the dataset's viability, achieving an average precision ([Formula: see text]) of 64.6, surpassing performance on Microsoft Common Objects in Context. Further experiments indicated that DSMs attached to this dataset could benefit from geo-deep-learning-based object detection algorithms for outfalls, achieving an [Formula: see text] exceeding 70. This study presents a UAS benchmark image dataset for outfall surveys, potentially advancing the application of deep learning in environmental protection.

Keywords

• Deep learning
• object detection dataset
• outfalls
• UAS imagery
• computer vision