36 research outputs found
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and
Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Crack is one of the most common road distresses which may pose road safety
hazards. Generally, crack detection is performed by either certified inspectors
or structural engineers. This task is, however, time-consuming, subjective and
labor-intensive. In this paper, we propose a novel road crack detection
algorithm based on deep learning and adaptive image segmentation. Firstly, a
deep convolutional neural network is trained to determine whether an image
contains cracks or not. The images containing cracks are then smoothed using
bilateral filtering, which greatly minimizes the number of noisy pixels.
Finally, we utilize an adaptive thresholding method to extract the cracks from
road surface. The experimental results illustrate that our network can classify
images with an accuracy of 99.92%, and the cracks can be successfully extracted
from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu
On the suitability of light field imaging for road surface crack detection
During traditional road surveys, inspectors capture images of pavement surface using cameras that produce 2D images, which can then be automatically processed to get a road surface condition assessment. In this paper the use of a light field imaging sensor is proposed, notably the Lytro Illum camera, to explore whether
the richer information captured by this imaging sensor provides additional cues useful to improve the automatic detection of road surface cracks. The preliminary results obtained indicate the interest in further exploring the disparity information captured by the light field sensor
Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management
Research on damage detection of road surfaces has been an active area of
re-search, but most studies have focused so far on the detection of the
presence of damages. However, in real-world scenarios, road managers need to
clearly understand the type of damage and its extent in order to take effective
action in advance or to allocate the necessary resources. Moreover, currently
there are few uniform and openly available road damage datasets, leading to a
lack of a common benchmark for road damage detection. Such dataset could be
used in a great variety of applications; herein, it is intended to serve as the
acquisition component of a physical asset management tool which can aid
governments agencies for planning purposes, or by infrastructure mainte-nance
companies. In this paper, we make two contributions to address these issues.
First, we present a large-scale road damage dataset, which includes a more
balanced and representative set of damages. This dataset is composed of 18,034
road damage images captured with a smartphone, with 45,435 in-stances road
surface damages. Second, we trained different types of object detection
methods, both traditional (an LBP-cascaded classifier) and deep learning-based,
specifically, MobileNet and RetinaNet, which are amenable for embedded and
mobile and implementations with an acceptable perfor-mance for many
applications. We compare the accuracy and inference time of all these models
with others in the state of the art