Visual Quality Inspection plays a crucial role in modern manufacturing environments as it ensures customer safety and satisfaction. The introduction of Computer Vision (CV) has revolutionized visual quality inspection by improving the accuracy and efficiency of defect detection. However, traditional CV models heavily rely on extensive datasets for training, which can be costly, time-consuming, and error-prone. To overcome these challenges, synthetic images have emerged as a promising alternative. They offer a cost-effective solution with automatically generated labels. In this paper, we propose a pipeline for generating synthetic images using domain randomization. We evaluate our approach in three real inspection scenarios and demonstrate that an object detection model trained solely on synthetic data can outperform models trained on real images.
SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection
University of Limerick（利默里克大学）；University of Galway（戈尔韦大学）；Valeo Vision Systems（法雷奥视觉系统公司）；Lero the Science Foundation Ireland Research Centre for Software（Lero爱尔兰科学基金会软件研究中心）
Zero-Shot Multi-Criteria Visual Quality Inspection for Semi-Controlled Industrial Environments via Real-Time 3D Digital Twin Simulation
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Costa Rica Institute of Technology（哥斯达黎加理工学院）；Gestalt Automation GmbH（Gestalt Automation有限公司）；Technical University Berlin（柏林工业大学）
Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA；Materials \& Manufacturing Systems Research Lab, General Motors, Warren, MI 48092, USA
Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry
Faurecia Clean Mobility（佛吉亚清洁出行）；Ecole Centrale de Lyon（里昂中央理工学院）；FEMTO-ST Institute, CNRS（法国国家科学研究中心FEMTO-ST研究所）；Univ. Bourgogne Franche-Comte (UBFC)（勃艮第弗朗什-孔泰大学）
Real-Time Wheel Detection and Rim Classification in Automotive Production
Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Institute of Information Theory and Automation, The Czech Academy of Sciences（捷克科学院信息理论与自动化研究所）
Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
Brno University of Technology（布尔诺理工大学）；VSB - Technical University of Ostrava（俄斯特拉发技术大学）；University of Ostrava（俄斯特拉发大学）；Mendel University in Brno（布尔诺孟德尔大学）
A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing
Institute for Machine Tools and Industrial Management (iwb), Technical University of Munich（慕尼黑工业大学机床与工业管理研究所）；MINES Paris, PSL University, Centre for Mathematical Morphology (CMM)（巴黎高科矿业学院，巴黎文理研究大学，数学形态学中心）；Fraunhofer Institute for Casting, Composite and Processing Technology IGCV（弗劳恩霍夫铸造、复合材料与加工技术研究所）
Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection
Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；National Technical University of Athens（雅典国立技术大学）；Philips Consumer Lifestyle BV（飞利浦消费者生活方式公司）；Qlector d.o.o.（Qlector公司）
Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions
Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples
Farmer School of Business, Miami University（迈阿密大学法默商学院）；College of Arts and Sciences, University of Dayton（Dayton大学文理学院）；Department of Mechanical Engineering, Politecnico di Milano（米兰理工学院机械工程系）；Mathematics & Statistics, Helmut Schmidt University（海姆-施密特大学数学与统计学系）；College of Engineering, University of Georgia（佐治亚大学工程学院）；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
Automated Counting of Stacked Objects in Industrial Inspection
EPFL（苏黎世联邦理工学院）；MBZUAI（马克斯·普朗克人工智能研究所）；Southern University of Science and Technology（南方科技大学）；Stony Brook University（石溪大学）；University of North Carolina（北卡罗来纳大学）
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
College of Electronics & Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China（电子与信息工程学院，上海智能自主系统研究院，智能自主系统国家重点实验室，智能自主系统前沿科学中心，同济大学，上海）
