16 skills found
fyangneil / Pavement Crack DetectionNo description available
qinnzou / DeepCrackDeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
shomnathsomu / Crack Detection OpencvCrack Detection On Highway Or Pavement Using OpenCV
juhuyan / CrackDataset DL HYPavement surface crack datasets for DL based crack detection
mqp2259 / EdmCrack600The dataset consists of 600 images about pavement cracks taken from roads in Edmonton Canada. They are all annotated at pixel level for crack detection
CHDyshli / PavementCrackDetectionNo description available
Karl1109 / SFIANThe code for paper "Selective Feature Fusion and Irregular-Aware Network for Pavement Crack Detection".
Desaiakshata / Asphalt Crack Classification Using Faster RCNNThis project is a thesis research work based on pavement crack detection using Faster RCNN Object detection model
shineho2014 / Unet Pavement Crack DetectionNo description available
prashant022 / Road Pavement Pothole Cracks DetectionRoad Pavement Pothole & Cracks Detection using Faster R-CNN and TFOD 2.0
albertchristianto / Defect DetectionMagnetic tile surface defect detection, NHA12D road/pavement crack detection
YuchunHuang / FPCNetCode and pretrained model of <FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture>
inuwamobarak / Pavement Degradation Resnet50A deep learning model demonstration to identify and classify pavement degradation. Created my dataset by Web Scraping over 10,000 images of pavement images
Mostafa-Nakhaei / Crack DetectionPavement Crack Detection Repository Based on Google Street View
DrEdwardLee / CrackDetectora robust graph network refining algorithm guided by multi-scale curvilinear structure filtering (CFGNR) for pavement crack detection
bharath-alavala123 / Automated Pavement Distress Detection Using YOLOv8This project presents a deep learning-based solution for detecting pavement surface distress using the YOLOv8 Medium (yolov8m.pt) object detection model. It is trained on the MAPSIA dataset, which contains 7,099 annotated images across 13 classes of asphalt surface defects, including cracks, potholes, and other road anomalies.
