机构

• 自动化研究所 [14]
• 半导体研究所 [5]
• 苏州纳米技术与纳米仿... [1]
• 沈阳自动化研究所 [1]
• 合肥物质科学研究院 [1]
• 近代物理研究所 [1]
• 更多

采集方式

• OAI收割 [23]
• iSwitch采集 [1]

内容类型

• 期刊论文 [24]

发表日期

• 2025 [1]
• 2024 [4]
• 2023 [2]
• 2022 [2]
• 2021 [5]
• 2020 [1]
• 更多

学科主题

• 半导体材料 [4]

筛选

浏览/检索结果: 共24条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Improved YOLO v5s-based detection method for external defects in potato 期刊论文  OAI收割 FRONTIERS IN PLANT SCIENCE, 2025, 卷号: 16, 页码: 15 作者:  Li, Xilong;  Wang, Feiyun;  Guo, Yalin;  Liu, Yijun;  Lv, Huangzhen   |  收藏  |  浏览/下载：32/0  |  提交时间：2025/04/07 potato  external defect  object detection  YOLO v5s  deep learning   Surface defect detection and semantic segmentation with a novel lightweight deep neural network 期刊论文  OAI收割 Measurement Science and Technology, 2024, 卷号: 35, 期号: 8, 页码: 14 作者:  Huang,Qiang;  Li,Fudong;  Yang,Yuequan;  Tao,Xian;  Li,Wei   |  收藏  |  浏览/下载：16/0  |  提交时间：2024/07/03 deep learning  surface defect detection  semantic segmentation  lightweight network   Enhancing Representation Power of Deep Neural Networks With Negligible Parameter Growth for Industrial Applications 期刊论文  OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024, 页码: 12 作者:  Chen, Liangming;  Jin, Long;  Shang, Mingsheng;  Wang, Fei-Yue   |  收藏  |  浏览/下载：20/0  |  提交时间：2024/05/30 Residual neural networks  Vectors  Noise  Biological neural networks  Medical services  Defect detection  Agriculture  Deep neural networks (DNNs)  industrial applications  ordinary differential equation (ODE)  representation power   Industry-oriented Detection Method of PCBA Defects Using Semantic Segmentation Models 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 6, 页码: 1438-1446 作者:  Yang Li;  Xiao Wang;  Zhifan He;  Ze Wang;  Ke Cheng   |  收藏  |  浏览/下载：13/0  |  提交时间：2024/05/22 Automated optical inspection (AOI)  deep learning  defect detection  printed circuit board assembly (PCBA)  semantic segmentation   Deep Industrial Image Anomaly Detection: A Survey 期刊论文  OAI收割 Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 104-135 作者:  Jiaqi Liu;  Guoyang Xie;  Jinbao Wang;  Shangnian Li;  Chengjie Wang   |  收藏  |  浏览/下载：6/0  |  提交时间：2024/04/23 Image anomaly detection, defect detection, industrial manufacturing, deep learning, computer vision   Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing 期刊论文  OAI收割 ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2023, 卷号: 80, 页码: 12 作者:  Li, Wenhao;  Zhang, Haiou;  Wang, Guilan;  Xiong, Gang;  Zhao, Meihua   |  收藏  |  浏览/下载：75/0  |  提交时间：2022/11/14 Wire and arc additive manufacturing  Defect detection  Online  Deep learning   A pixel-level deep segmentation network for automatic defect detection 期刊论文  OAI收割 EXPERT SYSTEMS WITH APPLICATIONS, 2023, 卷号: 215, 页码: 11 作者:  Yang, Lei;  Xu, Shuai;  Fan, Junfeng;  Li, En;  Liu, Yanhong   |  收藏  |  浏览/下载：32/0  |  提交时间：2023/02/22 Defect detection  Deep convolutional neural network  U-shape network  ConvLSTM network   Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures 期刊论文  OAI收割 MACHINES, 2022, 卷号: 10, 期号: 12, 页码: 17 作者:  Wei, Jing;  Zhang, Zhengtao;  Shen, Fei;  Lv, Chengkan   |  收藏  |  浏览/下载：43/0  |  提交时间：2023/03/20 industrial manufacturing  deep learning  data augmentation  defect generation  defect detection   A nondestructive automatic defect detection method with pixelwise segmentation 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2022, 卷号: 242, 页码: 12 作者:  Yang, Lei;  Fan, Junfeng;  Huo, Benyan;  Li, En;  Liu, Yanhong   |  收藏  |  浏览/下载：34/0  |  提交时间：2022/06/10 Defect detection  Deep architecture  Image segmentation  Attention fusion  Residual dense connection convolution  network   Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison 期刊论文  OAI收割 ELECTRONICS, 2021, 卷号: 10, 期号: 21, 页码: 14 作者:  Peng, Zhengrui;  Gong, Xinyi;  Wei, Bengang;  Xu, Xiangyi;  Meng, Shixiong   |  收藏  |  浏览/下载：21/0  |  提交时间：2021/12/28 fabric defect  unsupervised learning  computer vision  deep learning