VGGNET BASED DETECTION OF ROAD DAMAGE TYPE USING DEEP NEURAL NETWORKS
DESIGN DETAILS
Damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. This Matlab design is based on Deep Convolutional Neural Networks (VGGNET) to train the damage detection model with the dataset and finally, using Matlab 2020a simulation results demonstrate that the type of damage can be classified into eight types with accuracy.
ROAD DAMAGE TYPES
1.Wheel Mark Part- D00
2.Construction Joint Part- D01
3.Lateral Equal Interval- D10
4.Construction Joint Part- D11
5.Partial Pavement, Overall Pavement- D20
6.Rutting, Bump, Pothole, Separation- D40
7.White Line Blur- D43
8.Cross Walk Blur- D44
REFERENCES
Reference Paper-1: Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
Author’s Name: Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto, Takehiro Kashiyama, and Hiroshi Omata
Source: Computer Vision and Pattern Recognition
Year: 2018
Reference Paper-2: A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy
Author’s Name: Haruki Tsuchiyaa, Shinji Fukuib, Yuji Iwahoria, Yoshitsugu Hayashia, Witsarut Achariyaviriyaa and, Boonserm Kijsirikul
Source: Elsevier
Year: 2019
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