flamato/awesome-defect-detection-and-classification

List of papers and codes for defect detection.

Awesome-Defect-Detection-and-Classification

List of papers and codes for defect detection.
More information about Awesome-Anomaly-Detection

The papers are sorted according to the category of inputs.
A dataset written at the end of the paper title is constructed by the paper's authors.
Dataset lists which are saparate from the papers are used in other papers or codes at least once.

Defect Detection

Radiographs of welds in aerospace components

• Dong, Xinghui, Chris J. Taylor, and Tim F. Cootes. "Small defect detection using convolutional neural network features and random forests." ECCV 2018 Workshop | paper

X-Ray

• Ferguson, Max K., et al. "Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning." Smart and sustainable manufacturing systems 2 (2018) | paper | git

  Dataset

  • GDXray+ | site

Sewer Pipe

• Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks | paper
• Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network | paper
• [Survey] Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment | paper
• Underground sewer pipe condition assessment based on convolutional T neural networks | paper
• CLASSIFICATION OF UNDERWATER PIPELINE EVENTS USING DEEP CONVOLUTIONAL NEURAL NETWORKS | paper | poster
• Obstruction level detection of sewer videos using convolutional neural networks | paper
• Deep learning-based damage detection for sewer pipe inspection using faster R-CNN | paper
• Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN | paper

CCTV inspection

• Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques | paper
• Deep Learning–Based Automated Detection of Sewer Defects in CCTV Videos | paper

Building structure (concrete crack, steel corrosion, bolt corrosion, and steel delamination)

• Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types | paper | project
• Deep Learning for Detecting Building Defects Using Convolutional Neural Networks | paper
• Vision-based Structural Inspection using Multiscale Deep Convolutional Neural Networks | paper
• Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types | paper
• [Survey] A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure | paper
• [Survey] Awesome-Surface-Defect-Detection | git
• Zhang, Lei, et al. "Road crack detection using deep convolutional neural network." ICIP 2016 | paper | data: Surface Crack
• Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding | paper

  Dataset

  • Bridge cracks | git
  • CrackForest: road surface | git

Film

• Novel Framework for Optical Film Defect Detection and Classification | paper

Steel

• Deshpande, Aditya M., Ali A. Minai, and Manish Kumar. "One-Shot Recognition of Manufacturing Defects in Steel Surfaces." NAMRC48 2020 | paper
• Nie, Zheng, Jiachen Xu, and Shengchang Zhang. "Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection." arXiv preprint arXiv:2004.04822 (2020) | paper

  Dataset

  • NEU Steel Surface Defect Detection | kaggle | git

Electrical Components

• Tabernik, Domen, et al. "Segmentation-based deep-learning approach for surface-defect detection." Journal of Intelligent Manufacturing 31.3 (2020): 759-776 | paper | git implemented | data: Kolektor Surface-Detect
• R. Ding, L. Dai, G. Li and H. Liu, "TDD-net: a tiny defect detection network for printed circuit boards," in CAAI Transactions on Intelligence Technology, vol. 4, no. 2, pp. 110-116, 6 2019, doi: 10.1049/trit.2019.0019 | paper | git

  Dataset

  • PCB Defect | site

Magnetic Tile

• Huang, Yibin, Congying Qiu, and Kui Yuan. "Surface defect saliency of magnetic tile." The Visual Computer 36.1 (2020): 85-96 | paper | git | data: Magnetic tile

etc

Apple

• Valdez, Paolo. "Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling." ICLR 2020 | paper

Dataset

• Weakly Supervised Learning for Industrial Optical Inspection | site

Code

• Defect-Detection-Classifier | git

Defect Classification

• Saiz, Fátima A., et al. "A Robust and Fast Deep Learning-Based Method for Defect Classification in Steel Surfaces." 2018 International Conference on Intelligent Systems (IS). IEEE, 2018. | paper
• Arikan, Selim, Kiran Varanasi, and Didier Stricker. "Surface Defect Classification in Real-Time Using Convolutional Neural Networks." arXiv preprint arXiv:1904.04671 (2019). | paper