ArticlePlant phenomics (Washington, D.C.)2022
BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 35 citations in OpenAlex.
- Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments.Frontiers in plant science · 2024Article
- SDC-DeepLabv3+: Lightweight and Precise Localization Algorithm for Safflower-Harvesting Robots.Plant phenomics (Washington, D.C.) · 2024Article
- A high-precision jujube disease spot detection based on SSD during the sorting process.PloS one · 2024Article
- Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants.Biophysical reviews · 2023Review
- FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.Plant phenomics (Washington, D.C.) · 2023Article
- An accurate green fruits detection method based on optimized YOLOX-m.Frontiers in plant science · 2023Article
- Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite of significant achievements made in the detection of target fruits, small fruit detection remains a great challenge, especially for immature small green fruits with a few pixels. The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environment. In this paper, we propose a balanced feature pyramid network (BFP Net) for small apple detection. This network can balance information mapped to small apples from two perspectives: multiple-scale fruits on the different layers of FPN and a characteristic of a new extended feature from the output of ResNet50 conv1. Specifically, we design a weight-like feature fusion architecture on the lateral connection and top-down structure to alleviate the small-scale information imbalance on the different layers of FPN. Moreover, a new extended layer from ResNet50 conv1 is embedded into the lowest layer of standard FPN, and a decoupled-aggregated module is devised on this new extended layer of FPN to complement spatial location information and relieve the problem of locating small apple. In addition, a feature Kullback-Leibler distillation loss is introduced to transfer favorable knowledge from the teacher model to the student model. Experimental results show that AP
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Registered trials
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