A dataset of grape multimodal object detection and semantic segmentation

The accuracy of grape picking point localization is dependent on grape detection and semantic segmentation network performance. 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. Additionally, the dataset offers 3954 labeled image samples extracted from the aforementioned multimodal video. By means of rotation, deflation, mis-slicing, panning, and Gaussian blur, the dataset can be augmented for the training implementation of mainstream deep learning models. The dataset can provide valuable basic data resources for multimodal fusion, grape semantic segmentation, and object detection, which have important practical application value for promoting research in the field of agricultural machinery and equipment intelligence.