[PDF][PDF] Predicting the defects in wooden structures by using pre-trained models of Convolutional Neural Network and Image Processing
Wood is the oldest material used in construction industry and wooden structures are often
exposed to harsh conditions of environment. This exposure lead to deterioration due to …
exposed to harsh conditions of environment. This exposure lead to deterioration due to …
[PDF][PDF] Detecting cracks with Convolution Neural Network (CNN) with Variable image dataset
Millions of dollars are spent annually to detect damages in demanding infrastructures,
including bridges, roads, and buildings. Natural disasters such as floods and earthquakes …
including bridges, roads, and buildings. Natural disasters such as floods and earthquakes …
[PDF][PDF] Timber Defect Identification: Enhanced Classification with Residual Networks.
This study investigates the potential enhancement of classification accuracy in timber defect
identification through the utilization of deep learning, specifically residual networks. By …
identification through the utilization of deep learning, specifically residual networks. By …
Predicting the Characteristics of Defects in Wood Structures Using Image Processing and CNN
These defects can occur in the form of cracks, pain deterioration, dampness, etc. due to
mechanical and weathering effects. Crack identification and categorization must be part of …
mechanical and weathering effects. Crack identification and categorization must be part of …