DEFECTNET The architecture of DefectNet is illustrated in Fig. 2. It combines two paths able to detect different target sizes. The first path makes use of the VGG-19 architecture and skip layer fusion creating fully convolutional network. It is an enhanced version of the architecture of the F...
(c) DefectNet method. Network Design Defect Finding Network It resumes the last full connection layer in the backbone network. Fig. 2. Structure of defect finding network. I: input image. D: defective images. D-SubNet: the rest of the object detection network except the backbone network. ...
The team also looked at DeepSEM-Net’s segmentation abilities—an important feature for identifying defect boundaries accurately, which helps in assessing their impact on the wafer. Through pixel-level segmentation, DeepSEM-Net could outline defects with a clarity that previous models struggled...
Here, we will upload our deep/machine learning models and 'workflows' (such as AtomNet, DefectNet, SymmetryNet, etc) that aid in automated analysis of atomically resolved images - pycroscopy/AICrystallographer
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To solve these problems, we propose an edge-guided and differential attention network (EGD-Net), which can highlight the defect areas by strengthening edge information and effectively eliminating background clutter. In the proposed network, the multi-scale features are first extracted. Then, a ...
EEE-Net: Efficient Edge Enhanced Network for Surface Defect Detection of Glass Yongqi Chen, Jiawei Pan, Jiayu Lei, Deyu Zeng, Zongze Wu, Changsheng Chen 2023 DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion ...
GMVG-netDue to imperfections in the welding process and external factors, weld defects can significantly impact the lifespan and reliability of equipment. Therefore, weld defect detection is a crucial step in industrial production. However, traditional weld defect detection algorithms suffer from low ...
Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot...
WFF-Net: Trainable weight feature fusion convolutional neural networks for surface defect detection Advanced Engineering Informatics Volume 64, March 2025, Page 103073 Purchase options CorporateFor R&D professionals working in corporate organizations. Academic and personalFor academic or personal use only. Lo...