Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductive in mass productions, an automatic and reliable defect detection not just enhances the quality control process but positively improves productivity. However, casting defect detection is a challenging task due to diversity and variation in defects' appearance. Convolutional neural networks (CNNs) have been widely applied in both image classification and defect detection tasks. Howbeit, CNNs with frequentist inference require a massive amount of data to train on and still fall short in reporting beneficial estimates of their predictive uncertainty. Accordingly, leveraging the transfer learning paradigm, we first apply four powerful CNN-based models (VGG16, ResNet50, DenseNet121, and InceptionResNetV2) on a small dataset to extract meaningful features. Extracted features are then processed by various machine learning algorithms to perform the classification task. Simulation results demonstrate that linear support vector machine (SVM) and multi-layer perceptron (MLP) show the finest performance in defect detection of casting images. Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs. UQ confusion matrix and uncertainty accuracy metric are also utilized to evaluate the predictive uncertainty estimates. Comprehensive comparisons reveal that UQ method based on VGG16 outperforms others to fetch uncertainty. We believe an uncertainty-aware automatic defect detection solution will reinforce casting productions quality assurance.
Automated Detection of hidden Damages and Impurities in Aluminum Die Casting Materials and Fibre-Metal Laminates using Low-quality X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning
University of Bremen（不来梅大学）；University of Siegen（锡根大学）；Fraunhofer IFAM（弗劳恩霍夫制造技术和先进材料研究所）
TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification
Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi, UAE（机器学习系，Mohamed bin Zayed人工智能大学（MBZUAI），阿布扎比，阿联酋）；Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi, UAE（计算机视觉系，Mohamed bin Zayed人工智能大学（MBZUAI），阿布扎比，阿联酋）
Decision Fusion Network with Perception Fine-tuning for Defect Classification
Zhengzhou University（郑州大学）；Engineering Research Center of Intelligent Swarm Systems, Ministry of Education（教育部智能集群系统工程研究中心）；National Supercomputing Center in Zhengzhou（国家超级计算郑州中心）；Beihang University（北京航空航天大学）
Hybrid Quantum-MambaVision: A Quantum-enhanced State Space Model for Calibrated Mixed-type Wafer Defect Detection
Odisha University of Technology and Research（奥里萨理工大学）；National Taiwan University of Science and Technology（台湾科技大学）；East China Normal University（东华大学）；Macau University of Science and Technology（澳门科技大学）
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（精密测量技术与仪器国家重点实验室，清华大学精密仪器系）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，华中科技大学）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Informative Data Selection with Uncertainty for Multi-modal Object Detection
Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；China University of Mining & Technology, Beijing（中国矿业大学（北京））；Beijing University of Chemical Technology（北京化工大学）
