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Fast detection method of green peach for application of picking robot

https://doi.org/10.1007/s10489-021-02456-6
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-snapshot
CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-01261-8_1openalex-snapshot
Group Normalization
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_8openalex-snapshot
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
inferredcites →https://doi.org/10.1007/978-3-030-58555-6_16openalex-snapshot
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_2openalex-snapshot
SSD: Single Shot MultiBox Detector
inferredcites →https://doi.org/10.1007/s10489-020-01818-wopenalex-snapshot
Light-YOLOv3: fast method for detecting green mangoes in complex scenes using picking robots
inferredcites →https://doi.org/10.1007/s11119-012-9292-3openalex-snapshot
Green citrus detection using fast Fourier transform (FFT) leakage
inferredcites →https://doi.org/10.1007/s11119-014-9361-xopenalex-snapshot
Texture-based fruit detection
inferredcites →https://doi.org/10.1016/j.asoc.2020.106077openalex-snapshot
Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction
inferredcites →https://doi.org/10.1016/j.asoc.2020.106114openalex-snapshot
Dynamic Histogram Equalization for contrast enhancement for digital images
inferredcites →https://doi.org/10.1016/j.bspc.2019.101677openalex-snapshot
A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization
inferredcites →https://doi.org/10.1016/j.comnet.2019.107036openalex-snapshot
Faster R-CNN for multi-class fruit detection using a robotic vision system
inferredcites →https://doi.org/10.1016/j.compag.2018.07.011openalex-snapshot
Immature green citrus fruit detection using color and thermal images
inferredcites →https://doi.org/10.1016/j.compag.2020.105553openalex-snapshot
Peach variety detection using VIS-NIR spectroscopy and deep learning
inferredcites →https://doi.org/10.1023/a:1007618119488openalex-snapshot
Soft Margins for AdaBoost
inferredcites →https://doi.org/10.1109/access.2020.2981823openalex-snapshot
Fast Method of Detecting Tomatoes in a Complex Scene for Picking Robots
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-snapshot
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
inferredcites →https://doi.org/10.1109/cvpr.2019.00091openalex-snapshot
Libra R-CNN: Towards Balanced Learning for Object Detection
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00358openalex-snapshot
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot
Fast R-CNN
inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot
Mask R-CNN
inferredcites →https://doi.org/10.1109/iccv.2019.00972openalex-snapshot
FCOS: Fully Convolutional One-Stage Object Detection
inferredcites →https://doi.org/10.1109/iccvw.2019.00011openalex-snapshot
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
inferredcites →https://doi.org/10.1109/icdar.2019.00140openalex-snapshot
HoughNet: Neural Network Architecture for Vanishing Points Detection
inferredcites →https://doi.org/10.1109/ijcnn.2002.1007516openalex-snapshot
SSVM: a simple SVM algorithm
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1137/18m1209854openalex-snapshot
Turning Big Data Into Tiny Data: Constant-Size Coresets for $k$-Means, PCA, and Projective Clustering
inferredcites →https://doi.org/10.15302/j-fase-2018241openalex-snapshot
Feature extraction of hyperspectral images for detecting immature green citrus fruit
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.30534/ijeter/2020/60852020openalex-snapshot
Ripe Fruit Detection and Classification using Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1605.06409openalex-snapshot
R-FCN: Object Detection via Region-based Fully Convolutional Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3985202openalex-extracted
Fcos-Cba: An Fcos-Based Model for Accurate Detection and Segmentation of Obscured Green Fruits
inferred← citeshttps://doi.org/10.2139/ssrn.4535836openalex-extracted
inferred← citeshttps://doi.org/10.1002/jsfa.13684openalex-snapshot
Research on the visual location method for strawberry picking points under complex conditions based on composite models
inferred← citeshttps://doi.org/10.1007/s10489-021-03148-xopenalex-snapshot
SDA-Net: a detector for small, densely distributed, and arbitrary-directional ships in remote sensing images
inferred← citeshttps://doi.org/10.1007/s11694-024-03001-yopenalex-snapshot
Detection of maturity of “Okubo” peach fruits based on inverted residual mobile block and asymptotic feature pyramid network
inferred← citeshttps://doi.org/10.1016/j.aej.2024.09.062openalex-snapshot
Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101139openalex-snapshot
ESLC-YOLOv8: Advancing real-time pineapple recognition with lightweight deep learning
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2024.07.002openalex-snapshot
Review of deep learning-based methods for non-destructive evaluation of agricultural products
inferred← citeshttps://doi.org/10.1016/j.compag.2022.106984openalex-snapshot
An accurate detection and segmentation model of obscured green fruits
inferred← citeshttps://doi.org/10.1016/j.compag.2024.108700openalex-snapshot
Detection network for multi-size and multi-target tea bud leaves in the field of view via improved YOLOv7
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110753openalex-snapshot
A visual recognition method for the growth posture of small tomatoes under a complex greenhouse background
inferred← citeshttps://doi.org/10.1109/icdacai65086.2024.00057openalex-snapshot
Mechanical and Electronic Engineering Design and Control Technology under Artificial Intelligence
inferred← citeshttps://doi.org/10.11922/11-6035.csd.2022.0055.zhopenalex-snapshot
A dataset of multi-modal peach images for object detection
inferred← citeshttps://doi.org/10.3389/fpls.2025.1545216openalex-snapshot
A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion
inferred← citeshttps://doi.org/10.3390/agriculture13091814openalex-snapshot
An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots
inferred← citeshttps://doi.org/10.3390/agriculture14101848openalex-snapshot
Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach
inferred← citeshttps://doi.org/10.3390/agronomy12061336openalex-snapshot
Advance of Target Visual Information Acquisition Technology for Fresh Fruit Robotic Harvesting: A Review
inferred← citeshttps://doi.org/10.3390/app13042691openalex-snapshot
A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7
inferred← citeshttps://doi.org/10.3390/foods13233917openalex-snapshot
Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality
inferred← citeshttps://doi.org/10.3390/horticulturae11111394openalex-snapshot
A Flat Peach Bagged Fruits Recognition Approach Based on an Improved YOLOv8n Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/s22186843openalex-snapshot
An Improved Grey Wolf Optimization with Multi-Strategy Ensemble for Robot Path Planning
inferred← citeshttps://doi.org/10.3390/s23239482openalex-snapshot
Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots
inferred← citeshttps://doi.org/10.34133/plantphenomics.0135openalex-snapshot
DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling
inferred← citeshttps://doi.org/10.4018/978-1-6684-9231-4.ch021openalex-snapshot
Future of Smart Agriculture Techniques and Applications
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