Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on river barriers' locations and types, particularly small barriers including weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD). It comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs), encompassing five classes of river barriers. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, which provide performance benchmarks for future applications. To the best of our knowledge, RBOD is the first publicly available dataset for river barrier object detection, providing a valuable resource for the understanding and management of river barriers.