ArticleFrontiers in robotics and AI2021
Fruit Detection and Pose Estimation for Grape Cluster-Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8.Frontiers in plant science · 2024Article
- A Variable Photo-Model Method for Object Pose and Size Estimation with Stereo Vision in a Complex Home Scene.Sensors (Basel, Switzerland) · 2023Article
- An improved YOLO v4 used for grape detection in unstructured environment.Frontiers in plant science · 2023Article
- Analysis of Depth Cameras for Proximal Sensing of Grapes.Sensors (Basel, Switzerland) · 2022Article
- Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review.Frontiers in plant science · 2022Review
- YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit.Frontiers in plant science · 2022Article
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Authors and funding
6 authors.
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Abstract
Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (
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