High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing University（重庆大学）；Inspur Yunzhou Industrial Internet Co., Ltd（浪潮云洲工业互联网有限公司）；Guoqi Zhimo (Chongqing) Technology Co., Ltd.（国汽智磨（重庆）科技有限公司）；Shenzhen University（深圳大学）
Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen, Jing Yu
In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demonstrated outstanding performance on the artificial leather defect dataset, achieving an impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a significant reduction of 5.2% - 7.2% in the error detection rate. Moreover, YOLOD also exhibited remarkable performance on the general MS-COCO dataset, with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% - 4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of YOLOD in both artificial leather defect detection and general object detection tasks, making it a highly efficient and effective model for real-world applications.
Integrated Neural Network and Machine Vision Approach For Leather Defect Classification
Feng Chia University（逢甲大学）；National Taipei University of Nursing and Health Sciences（台北护理健康大学）；Xiamen University（厦门大学）；Xiamen University Malaysia（厦门大学马来西亚分校）
Efficient Neural Network Approaches for Leather Defect Classification
National Taipei University of Nursing and Health Sciences（国立台北护理健康大学）；Feng Chia University（逢甲大学）；Xiamen University（厦门大学）；Ton Duc Thang University（孙德胜大学）
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）
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection
Digital Sense（数字感知）；Universidad de la República（乌拉圭共和国大学）；IIE, Facultad de Ingeniería Universidad de la República（乌拉圭共和国大学工程学院IIE研究所）；Universidad Católica del Uruguay（乌拉圭天主教大学）
SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection
Waterford Institute South East Technological University Waterford, Ireland（沃特福德研究所 南东技术大学 爱尔兰沃特福德）；Faculty of Innovation Engineering Macau University of Science（创新工程学院 澳门科学技术大学）
