djene-mengistu / Machine-Vision-and-Anomaly-Detection-Papers

This repo contains state-of-the-art deep learning models for industrial anomaly detection, defect segmentation, detection, and classification, with other industrial machine vision applications.

${\color{blue}------>Please-comeback-and-visit-the-updated-list-as-new-papers-are-added-everyday<------}$

Machine-Vision-and-Anomaly-Detection-Papers-Codes

• Machine-Vision-and-Anomaly-Detection-Papers-Codes
  • 1. Introduction and background
  • 2. Industrial anomaly detection
  • 3. Classification, Detection and Segmentation Models
  • 4. Semi-supervised and weakly-supervised learning

Introduction-and-background

This repository consists of recent state-of-the-art deep learning networks for industrial machine vision application. The transformation of manufacturing system towards the intelligent manufacturing focuses on automation and the use of advanced technologies such as AI with robots and advanced machines for greater efficiency and precision. The AI system will allow for an optimized production process, smart decisions, real-time information, preventive maintenance, and self-prognosis of the production processes. With availability of big data and advanced computing equipment, and technologies, the deep learning application has been one of the highly researched areas in the scientific world in the past few years. Promoting this applications, this repositoty presents recent influential works related to deep learning applications on the area of anomaly detection, and other industrial machine vision applications.

Industrial-anomaly-detection

Industrial anomaly detection is a critical component of modern industrial processes that involve the monitoring and analysis of data to identify abnormal behavior or deviations from expected patterns within industrial systems. Although various anomalies can be investigated, this repository presents deep learning application for surface anomaly detection for industrial products. Most of the methods presented uses image datasets to identify defective or anomolous parts of the product.

Year	Title/Source	Journal/Conference	Code
2024	Deep Industrial Image Anomaly Detection: A Survey	Machine Intelligence Research	Not available
2024	AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection	arxiv	Pytorch
2023	Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models	Computers & Industrial Engineering	Not available
2023	AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models	arxiv	Pytorch
2023	FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks	CVPR	Pytorch
2023	Prototypical Residual Networks for Anomaly Detection and Localization	CVPR	Not available
2023	A deep convolutional network combining layerwise images and defect parameter vectors for laser powder bed fusion process anomalies classification	Journal of Intelligent Manufacturing	Not available
2022	SimpleNet: A Simple Network for Image Anomaly Detection and Localization	CVPR	Pytorch
2021	Towards Total Recall in Industrial Anomaly Detection	CVPR	Pytorch

⬆ back to top

Classification-Detection-and-Segmentation-Models

Defect classification, detection, and segmentation are important tasks in various industries, particularly in manufacturing and quality control processes. These tasks involve identifying and categorizing defects in products or materials. Deep learning-based defect classification involves identifying types of defects in a product or simply identifying wether a product is defective or not. Detection involves localization and classification of defects, while defect segmetnation involves identification and localization of defects at a pixel-level. Recent state-of-the-art methods involving these taks are presented in this repository.

Year	Title/Source	Journal/Conference	Code
2023	Deep learning-based automated steel surface defect segmentation: a comparative experimental study	Multimedia Tools and Applications	Pytorch
2023	Deep CNN-based visual defect detection: Survey of current literature	Computers in Industry	Not available
2023	Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning	Computers in Industry	Not available
2023	WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects	Computers in Industry	Not available
2023	Automatic Defect Classification Using Semi-Supervised Learning With Defect Localization	IEEE Transactions on Semiconductor Manufacturing	Not available
2023	A novel micro-defect classification system based on attention enhancement	Journal of Intelligent Manufacturing	Not available
2023	Spatial Attention Enhanced Wafer Defect Classification Algorithm for Tiny Defects	IEEE ICAIT	Not available
2022	Deep Adversarial Data Augmentation for Fabric Defect Classification With Scarce Defect Data	IEEE Transactions on Instrumentation and Measurement	Not available
2022	Fabric defect classification using prototypical network of few-shot learning algorithm	Computers in Industry	Not available
2022	Automated steel surface defect detection and classification using a new deep learning-based approach	Journal of Intelligent Manufacturing	Not available
2022	Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation	Journal of Intelligent Manufacturing	Not available
2022	A systemic comparison between using augmented data and synthetic data as means of enhancing wafermap defect classification	Computers in Industry	Not available
2022	Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review	Journal of Intelligent Manufacturing	Not available
2021	Improvement of Multi-Lines Bridge Defect Classification by Hierarchical Architecture in Artificial Intelligence Automatic Defect Classification	IEEE Transactions on Semiconductor Manufacturing	Not available

⬆ back to top

Semi-supervised-and-weakly-supervised-learning

Semi-supervised learning and weakly supervised learning are two approaches to machine learning that address scenarios where obtaining fully labeled training data is challenging or expensive. Hence, this repository presents state-of-the-art semi-supervised and weakly-supervised methods proposed for the task of intelligent industrial inspection.

Year	Title/Source	Journal/Conference	Code
2023	Uncertainty-aware and dynamically-mixed pseudo-labels for semi-supervised defect segmentation	Computers in Industry	Pytorch
2022	A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification	Journal of Intelligent Manufacturing	Not available
2022	Semisupervised Defect Segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels	IEEE TII	Pytorch
2019	Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters	Computers in Industry	Not available

⬆ back to top