Fast and Accurate Detection of Forty Types of Fruits and Vegetables: Dataset and Method

Accurate detection of fruits and vegetables is a key task in agricultural automation. However, existing detection methods typically focus on identifying a single type of fruit or vegetable and are not equipped to handle complex and diverse environments. To address this, we introduce the first large-scale benchmark dataset for fruit and vegetable detection—FV40. This dataset contains 14,511 images, covering 40 different categories of fruits and vegetables, with over 100,000 annotated bounding boxes. Additionally, we propose a novel framework for fruit and vegetable detection—FVRT-DETR. Based on the Transformer architecture, this framework features an end-to-end real-time detection algorithm. FVRT-DETR enhances feature extraction by integrating the Mamba backbone network and improves detection performance for objects of varying scales through the design of a multi-scale deep feature fusion encoder (MDFF encoder) module. Extensive experiments show that FVRT-DETR performs excellently on the FV40 dataset. In particular, it demonstrates a significant performance advantage in detection of small objects and under complex scenarios. Compared to existing state-of-the-art detection algorithms, such as YOLOv10, FVRT-DETR achieves better results across multiple key metrics. The FVRT-DETR framework and the FV40 dataset provide an efficient and scalable solution for fruit and vegetable detection, offering significant academic value and practical application potential.