Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

by   Zhun Fan, et al.

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.



There are no comments yet.


page 9

page 10

page 11

page 12


Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safe...

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

Road intersections data have been used across different geospatial appli...

Automatic Detection of Rail Components via A Deep Convolutional Transformer Network

Automatic detection of rail track and its fasteners via using continuous...

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Environmental air quality affects people's life, obtaining real-time and...

An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids

Smart grids extremely rely on Information and Communications Technology ...

Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3

Automatic measurement of functional trait data from digitized herbarium ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.