ZHANGKEON's repositories
crack_segmentation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
CS543_project_Image-based-Localization-of-Bridge-Defects-with-AR-Visualization
Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. To address these limitations, we investigate a computer vision‐based approach that employs SIFT keypoint matching on collected images of defects against a pre-existing reconstructed 3D point cloud of the bridge. We also investigate methods of reducing computation time with ML-based and conventional CV methods of segmentation to eliminate redundant keypoints. Our project successfully localizes the defect images and achieves a savings in runtime from filtering keypoints.
Deep-Learning-and-Computer-Vision-for-Structural-Crack-Detection-And-Classification
Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance
FCN_for_crack_recognition
An application FCN for crack recogntion using tensorflow
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
severstal
Severstal: Steel Defect Detection
unet-tensorflow-keras
A concise code for training and evaluating Unet using tensorflow+keras