Welcome to the Robotic Sensor Network Laboratories (RSN) MinneApple segmentation challenge. This challenge is only one of our computer vision for precision agriculture challenges. For our challenge on fruit detection and fruit counting, please visit the respective websites.
Yield mapping in orchard environments from RGB images is a challenging and important problem, where many current states of the art algorithms fail. We want to push state of the art for computer vision algorithms that can handle large numbers of small fruits in cluttered and occluded outdoor environments.
The competition uses the recently released MinneApple dataset, consisting of roughly 1000 annotated images for fruit detection and segmentation and 60000 images for patch-based fruit counting. The dataset contains a large number of different scenarios, with varying varieties of apple, illumination conditions, and occlusion scenarios. We provide 631 images for training/validation, and the rest are used for testing. Participants are encouraged to generate their own training/validation splits form the data for which we provide labels. We do not make the labels for the testing set available to ensure fair competition.
Please make sure to follow the submission instructions in the Evaluation section.
The following metrics are used for characterizing the performance of segmentation methods on MinneApple. The challenge winner is determined based on the highest Mean Intersection over Union (IoU) score. An overview over these metrics can be found in [Shelhamer PAMI 2016]
The server expects a single ZIP archive containing segmentation masks (grayscale images). You should submit one segmentation mask per image in the test set. Make sure that the name of the mask is the same as the name of the original image file. The structure of your ZIP file should look like this:
The images and annotations in this dataset belong to the Robotic Sensor Network Laboratory at the University of Minnesota and are licensed under an Attribution-NonCommercial-ShareAlike 3.0 United States license.
Start: Oct. 28, 2019, midnight
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