A dataset of multimodal pear images for target detection

Therefore, we have built a multi-modal image dataset for crisp pear target detection, which covers several aspects of crisp pears including multi-modal images acquisition, classification, labeling, storage and usage. A total of 5.86GB of high-quality multi-modal image data were collected to the multi-modal image dataset, under the different natural circumstances of cloudy and sunny, daytime and nighttime, lighting and backlighting, naked and bagging, motion blurring scenes.

Different from the existing fruit datasets that only provide visible images, this image data set is composed of traditional RGB images, as well as corresponding depth images and infrared images, which can be used as training samples for modeling intelligent fruit multimodal vision. As a standard dataset for deep learning modeling in big data environment, this image dataset offers valuable basic data resource for the research directions of multi-modal image data fusion and target detection, with the important practical application potential of promoting the research in the field of fruit target detection.