ArticleFrontiers in plant science2022
Apple detection and instance segmentation in natural environments using an improved Mask Scoring R-CNN Model.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 24 citations in OpenAlex.
- Ta-YOLO: overcoming target blocked challenges in greenhouse tomato detection and counting.Frontiers in plant science · 2025Article
- CabbageNet: Deep Learning for High-Precision Cabbage Segmentation in Complex Settings for Autonomous Harvesting Robotics.Sensors (Basel, Switzerland) · 2024Article
- Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors at 2 institutions in 1 country.
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Abstract
The accurate detection and segmentation of apples during growth stage is essential for yield estimation, timely harvesting, and retrieving growth information. However, factors such as the uncertain illumination, overlaps and occlusions of apples, homochromatic background and the gradual change in the ground color of apples from green to red, bring great challenges to the detection and segmentation of apples. To solve these problems, this study proposed an improved Mask Scoring region-based convolutional neural network (Mask Scoring R-CNN), known as MS-ADS, for accurate apple detection and instance segmentation in a natural environment. First, the ResNeSt, a variant of ResNet, combined with a feature pyramid network was used as backbone network to improve the feature extraction ability. Second, high-level architectures including R-CNN head and mask head were modified to improve the utilization of high-level features. Convolutional layers were added to the original R-CNN head to improve the accuracy of bounding box detection (
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