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Faster Region Convolutional Neural Network with Attentional Feature Fusion for Small Defect Detection

https://doi.org/10.2139/ssrn.4750909
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-11018-5_35openalex-extracted
Small Defect Detection Using Convolutional Neural Network Features and Random Forests
inferredcites →https://doi.org/10.1007/978-3-030-17795-9_10openalex-extracted
Deep Learning vs. Traditional Computer Vision
inferredcites →https://doi.org/10.1007/s001700070055openalex-extracted
Automated Surface Inspection Using Gabor Filters
inferredcites →https://doi.org/10.1007/s10845-020-01591-0openalex-extracted
Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing
inferredcites →https://doi.org/10.1007/s10845-020-01670-2openalex-extracted
A steel surface defect inspection approach towards smart industrial monitoring
inferredcites →https://doi.org/10.1007/s10845-020-01704-9openalex-extracted
A real-time defective pixel detection system for LCDs using deep learning based object detectors
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-extracted
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1016/j.apsusc.2015.05.033openalex-extracted
An improved Otsu method using the weighted object variance for defect detection
inferredcites →https://doi.org/10.1016/j.asoc.2016.10.030openalex-extracted
Automatic surface defect detection for mobile phone screen glass based on machine vision
inferredcites →https://doi.org/10.1016/j.asoc.2022.109743openalex-extracted
A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets
inferredcites →https://doi.org/10.1016/j.asoc.2023.110578openalex-extracted
Steel surface defect detection based on self-supervised contrastive representation learning with matching metric
inferredcites →https://doi.org/10.1016/j.engappai.2023.106359openalex-extracted
PCB defects target detection combining multi-scale and attention mechanism
inferredcites →https://doi.org/10.1016/j.engappai.2023.107387openalex-extracted
An automated defect detection method for optimizing industrial quality inspection
inferredcites →https://doi.org/10.1016/j.engappai.2023.107717openalex-extracted
A systematic review of deep learning approaches for surface defect detection in industrial applications
inferredcites →https://doi.org/10.1016/j.jmsy.2018.01.003openalex-extracted
Deep learning for smart manufacturing: Methods and applications
inferredcites →https://doi.org/10.1016/j.knosys.2023.111066openalex-extracted
GRA-Net: Global receptive attention network for surface defect detection
inferredcites →https://doi.org/10.1016/j.patrec.2023.06.010openalex-extracted
Multi-scale attention and dilation network for small defect detection
inferredcites →https://doi.org/10.1016/s0963-8695(97)00042-xopenalex-extracted
An automated radiographic NDT system for weld inspection: Part II—Flaw detection
inferredcites →https://doi.org/10.1038/nature14539openalex-extracted
Deep learning
inferredcites →https://doi.org/10.1109/28.871274openalex-extracted
Fabric defect detection by Fourier analysis
inferredcites →https://doi.org/10.1109/83.661196openalex-extracted
Adaptive thresholding by variational method
inferredcites →https://doi.org/10.1109/access.2019.2932731openalex-extracted
An Improved Faster R-CNN for Small Object Detection
inferredcites →https://doi.org/10.1109/access.2020.3001349openalex-extracted
Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-extracted
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-extracted
Feature Pyramid Networks for Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-extracted
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-extracted
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00060openalex-extracted
Selective Kernel Networks
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-extracted
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/jproc.2023.3238524openalex-extracted
Object Detection in 20 Years: A Survey
inferredcites →https://doi.org/10.1109/tii.2016.2522191openalex-extracted
Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones
inferredcites →https://doi.org/10.1109/tmm.2021.3074273openalex-extracted
Extended Feature Pyramid Network for Small Object Detection
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-extracted
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tsmc.1973.4309314openalex-extracted
Textural Features for Image Classification
inferredcites →https://doi.org/10.1109/wacv48630.2021.00360openalex-extracted
Attentional Feature Fusion
inferredcites →https://doi.org/10.3233/fi-2000-411207openalex-extracted
The Watershed Transform: Definitions, Algorithms and Parallelization Strategies
inferredcites →https://doi.org/10.3390/ma13245755openalex-extracted
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges
inferredcites →https://doi.org/10.3390/s20051459openalex-extracted
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
inferredcites →https://doi.org/10.3390/s21093031openalex-extracted
Small Object Detection in Traffic Scenes Based on Attention Feature Fusion
inferredcites →https://doi.org/10.4230/oasics.ng-res.2024.2openalex-extracted
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-extracted
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1709.01507openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.48550/arxiv.1905.11946openalex-extracted
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
inferredcites →https://doi.org/10.5194/jsss-9-167-2020openalex-extracted
Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing
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