Abstract
Objectives: The proposed research work detects road cracks in a given set of images. In addition, it identifies the longitudinal type of crack in given crack image. Methods: The study mainly focuses on implementing a road crack detection technique using Convolutional Neural Networks. Findings: The proposed model is able to distinguish between crack and non-crack images and also able to classify the longitudinal crack from other given crack images. Novelty: Proposed road crack detection technique provides high accuracy compared to earlier standard techniques. Keywords: Road crack detection; CNN (Convolutional Neural Networks); support vector machines (SVM); deep learning; classification; image processing
Highlights
There are lot of road accidents happening round the world
The main problem lies within the road, which causes the standard degradation of road surface resulting into cracks
This paper focuses on studying and comparing different methods and technologies used in crack detection
Summary
The main problem lies within the road, which causes the standard degradation of road surface resulting into cracks. These cracks mainly result due to environmental factors and improper maintenance of the road. Due to this improper maintenance, development of cracks on the surface is the major issue. These cracks can further degrade the road quality by forming potholes[1]. It’s slower, automatic inspection is preferred because it provides better speed, low cost and more accuracy
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