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Found 7 papers, 1 papers with code
Apple Counting using Convolutional Neural Networks
Estimating accurate and reliable fruit and vegetable counts from images in real-world settings, such as orchards, is a challenging problem that has received significant recent attention.
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion
We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection.
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
The fruits are labeled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation.
Semantics-Aware Image to Image Translation and Domain Transfer
Image to image translation is the problem of transferring an image from a source domain to a different (but related) target domain.
A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards
We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods.
Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows
Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides.
Robotics
Vision-Based Preharvest Yield Mapping for Apple Orchards
Additionally, we report merged fruit counts from both sides of the tree rows.
