RPV11K: A Benchmark for Joint Product-Vacancy Detection in Retail Scenarios

In intelligent retail, accurate detection of densely arranged products and shelf vacancies in unstructured environments remains a critical challenge. This paper introduces RPV11K, a large-scale benchmark dataset (11,743 im-ages, 1.87M annotations) designed for joint product and vacancy detection in real-world retail scenarios. We develop a novel edge-deployable framework, DetectRPV, integrating lightweight YOLO variants and systematic data augmentation. Experimental results on the Jetson edge platform show that YOLOv1-medium achieves the best performance with mAP50 of 80.6% and mAP of 55.1%, while maintaining an inference speed of 52ms. RPV11K, the first dataset to explicitly model both product and vacancy categories under dense occlusion and diverse lighting, provides a rigorous evaluation benchmark for automated shelf management systems. Our work bridges the gap between academic research and industrial deployment by establishing a benchmark for real-time shelf monitor-ing with metrics relevant to automated retail operations. The code and data will be made available on https://github.com/Cy1oong/RPV11K.