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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.

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

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

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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

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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

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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.

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