Global Road Damage Detection: State-of-the-art Solutions

17 November 2020

Papers citing "Global Road Damage Detection: State-of-the-art Solutions"

7 / 7 papers shown

Title
An Efficient and Scalable Deep Learning Approach for Road Damage Detection Sadra Naddaf-sh M.-Mahdi Naddaf-Sh Amir R. Kashani H. Zargarzadeh 42 58 0 18 Nov 2020
Road Damage Detection using Deep Ensemble Learning Keval Doshi Yasin Yılmaz 33 54 0 30 Oct 2020
Road Damage Detection and Classification with Detectron2 and Faster R-CNN Vung V. Pham Chau Pham Tommy Dang 30 83 0 28 Oct 2020
FasterRCNN Monitoring of Road Damages: Competition and Deployment T. Hascoet Yihao Zhang Andreas Persch R. Takashima T. Takiguchi Y. Ariki 57 23 0 22 Oct 2020
Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis Vishal Mandal Abdul Rashid Mussah Y. Adu-Gyamfi 41 53 0 21 Oct 2020
Transfer Learning-based Road Damage Detection for Multiple Countries Deeksha M. Arya Hiroya Maeda S. Ghosh Durga Toshniwal A. Mraz Takehiro Kashiyama Yoshihide Sekimoto Indian Institute of Technology Roorkee 49 75 0 30 Aug 2020
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone Hiroya Maeda Y. Sekimoto Toshikazu Seto Takehiro Kashiyama Hiroshi Omata 50 675 0 29 Jan 2018