ROAD DAMAGE DETECTION TYPE USING DEEP NEURAL NETWORKS INCEPTION NEWORK
DESIGN DETAILS
Pavement condition assessment is an important process in road maintenance, monitoring and traffic safety. However, current pavement condition assessment methods are time consuming and usually implemented manually. Pavement distress is becoming one of the biggest problems in road network systems. An efficient and effective detection method is needed to identify these pavement problems on the road. Additionally, road damage detection is much more complicated than other infrastructure element detection. Traditionally road damage detection methods are complex and inefficient, and the road inventory was performed by field inspection, now it is replaced by the evaluation of system images. Therefore, the purpose of this design is to create an efficient and effective road damage detection model to identify road damages based on pavement images. The acquired images are still a significant source of temporal condition of the pavement surface. The automatization of road damage detection is highly necessary because it could decrease workload, and therefore, maintenance costs. In this Matlab design Inception network is used automatic road damage detection from sample images were tested. The objective of this design is to develop and test the workflow for the street view image road damage detection.
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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