.png){kind=icon}
返回
AFM Imaging Defect Detection and Classification Using Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3459868.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Atomic Force Microscopy (AFM) has been a broadly used platform for high-resolution imaging and mechanical characterization of a wide range of samples. However, this technique can be time-consuming and heavily relies on constant human supervision and human insight for data acquisition and analysis. Recent advancement in artificial intelligence (AI) provides the potential for efficient data analysis for AFM applications. The fusion of AFM with AI for effective image analysis and classification still remains an ongoing research endeavor. In this study, we present a novel AFM image defect detection and classification framework, AFM_YOLO-ResNet, using advanced deep learning (DL) techniques. Central to our approach is a highly integrated DL model that consists of a YOLO image defect detection layer and a ResNet feature extraction and classification layer. The proposed AFM_YOLO-ResNet framework is trained with expert annotated AFM images, and prepared to assess future AFM images from similar samples. Performance of AFM_YOLO-ResNet was validated for AFM image defect detection and classification, and compared with three commonly used transfer learning and computer vision models (Googlenet, Darknet, and YOLOv8). The results with high training and validation accuracies demonstrated that the AFM_YOLO-ResNet framework greatly improves the AFM imaging analysis efficiency.
Keyword:
AFM
deep learning
image analysis
identification
classification
AFM
deep learning
image analysis
identification
classification
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
War on the Agenda: The Gaza Conflict and Communication in the 2009 Elections议程上的战争：加沙冲突与2009年选举中的传播
PREAI
分享收藏Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments基于通道剪枝的YOLO v4深度学习算法在自然环境下苹果花朵实时准确检测中的应用
PREAI
分享收藏Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography ScenariosUAV-YOLOv8: 基于改进YOLOv8的无人机航拍场景小目标检测模型SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion ModeSF-YOLOv5: 基于改进特征融合模式的轻量级小目标检测算法SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容