Instituto de Pesquisas Eldorado（埃尔多拉多研究所）；HP Labs, HP Inc.（惠普实验室）
Augusto C. Valente, Cristina Wada, Deangela Neves, Deangeli Neves, Fábio V. M. Perez, Guilherme A. S. Megeto, Marcos H. Cascone, Otavio Gomes, Qian Lin
Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.
Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background
School of Resources and Environment, University of Electronic Science and Technology of China（电子科技大学资源与环境学院）；South China Normal University（华南师范大学）；Sichuan Changhong Electric Co., Ltd.（四川长虹电子控股集团有限公司）；University of Twente（特温特大学）
Weakly Supervised Panoptic Segmentation for Defect-Based Grading of Fresh Produce
Empa, Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室（Empa））；Swiss Data Science Center（瑞士数据科学中心）；ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；Constructor University（康斯特大学）；Innoterra BioScience Private Limited（因诺泰拉生物科学私人有限公司）
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells
Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；Hevel LLC（Hevel有限责任公司）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡国立电工大学“LETI”）；Unigreen Energy LLC（Unigreen能源有限责任公司）；HSE University（高等经济大学）；Sber AI Lab（俄罗斯联邦储蓄银行人工智能实验室）
KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning
Canizaro Livingston Gulf States Center for Environmental Informatics, the University of New Orleans（坎扎里奥利文斯顿湾州环境信息中心，新奥尔良大学）；Center for Geospatial Sciences, Naval Research Laboratory, Stennis Space Center, Hancock County, Mississippi, USA（地理空间科学中心，海军研究实验室，斯蒂尼斯太空中心，密西西比州汉克县，美国）；US Army Corps of Engineers, Engineer Research and Development Center, Vicksburg, Mississippi, USA（美国陆军工程兵团，工程研究与发展中心，维克斯堡，密西西比州，美国）
Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception
School of Instrumentation Science and Engineering, Harbin Institute of Technology（仪器科学与工程学院，哈尔滨工业大学）；Harbin Institute of Technology Suzhou Research Institute（哈尔滨工业大学苏州研究所）
Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation
Canizaro Livingston Gulf States Center for Environmental Informatics, the University of New Orleans（Canizaro Livingston Gulf States Center for Environmental Informatics，新奥尔良大学）；Graz University of Technology（格拉茨技术大学）；US Army Corps of Engineers, Engineer Research and Development Center（美国陆军工程兵团，工程研究与发展中心）
No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction
Shanghai University（上海大学）；Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Wenzhou Institute of Shanghai University（上海大学温州研究院）
Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process
Politecnico Di Milano（米兰理工大学）；AI Fab Lab（AI制造实验室）；National Taiwan University of Science and Technology（台湾科技大学）；Bahir Dar University（巴赫达尔大学）；NIT Warangal（瓦朗加尔国家理工学院）
Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images
Shenzhen University（深圳大学）；Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen)（广东人工智能与数字经济实验室（深圳））；Shenzhen Institute of Technology（深圳技术学院）
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection
Faculty of Electronics and Telecommunications, University of Engineering and Technology, Vietnam National University（电子与电信学院，工程与技术大学，越南国家大学）；School of Information Science, Japan Advanced Institute of Science and Technology（信息科学学院，日本先进科学和技术研究院）；Faculty of Mechatronics, SMAE, Hanoi University of Industry（机械学院，SMAE，河内工业大学）
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
Institute for Infocomm Research (I2R), the Agency for Science, Technology and Research (A*STAR)（资讯通信研究院（I2R），新加坡科技研究局（A*STAR））；Zhejiang University（浙江大学）
SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation
School of Artificial Intelligence, Jiangxi Normal University（江西师范大学人工智能学院）；Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing（江西省智能信息处理与情感计算重点实验室）
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
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）；Tianjin University（天津大学）；The University of Sydney（悉尼大学）；Sydney AI Centre（悉尼人工智能中心）
