A dataset of grape multimodal object detection and semantic segmentation

However, in practical application scenarios, the accuracy and segmentation precision of grape targets based on visible light images are susceptible to light variations and complex environments, often performing poorly. Moreover, grapes grow in bunches, and the existing multimodal datasets for apples and pears can hardly meet the recognition needs of bunch-shaped grapes.

The construction of visible, depth, and near-infrared multimodal object detection and semantic segmentation datasets of grapes is crucial to exploring better recognition rates and stronger generalization capabilities for grape detection and semantic segmentation models. This dataset, totaling about 39.08 GB, contains high-quality multimodal video stream data of green and purple grapes, including six varieties, under different illumination and obscuration conditions.