198 research outputs found
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
Crack detection in concrete tunnels using a gabor filter invariant to rotation
Producción CientíficaIn this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.Ministerio de Economía y Competitividad under project Ref. IPT-2012-0980-370000Ministerio de Ciencia e Innovación, research project Ref. DPI2014-56500Junta de Castilla y León Ref. VA036U14
Pixel level pavement crack detection using deep convolutional neural network with residual blocks
Road condition monitoring, such as surface defects and pavement cracks detection, is an important task in road management. Automated road surface defect detection is also a challenging problem in computer vision and machine learning research due to the large variety of pavement crack structures, variable lighting conditions, interfering objects on the road surface such as trashes, fallen tree leaves and branches. In this work, we develop a deep learning-based method for automated road surface defect and pavement crack detection. We design a deep convolutional neural network based on using residual blocks to predict the heatmaps which indicate the location and intensity of defects and cracks. To reduce false detection rates, we couple this heatmap prediction network with a binary classification network which is able to determine if the input image patch is normal or has defects. We test our method on the CFD benchmark dataset. Experiment results show that the proposed network is very effective for pavement crack detection and has more advanced performance than other methods.by Yu HouIncludes bibliographical reference
Performance Comparison of Hybrid CNN-SVM and CNN-XGBoost models in Concrete Crack Detection
Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the intent of this study is to address the above problem using two-hybrid machine learning models and classify the concrete digital images into having cracks or non-cracks. The Convolutional Neural Network is used in this study to extract features from concrete pictures and use the extracted features as inputs for other machine learning models, namely Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost). The proposed method is evaluated on a collection of 40000 real concrete images, and the experimental results show that application of XGBoost classifier to CNN extracted image features include an advantage over SVM approach in accuracy and achieve a relatively better performance than a few existing methods
Learning sound representations using trainable COPE feature extractors
Sound analysis research has mainly been focused on speech and music
processing. The deployed methodologies are not suitable for analysis of sounds
with varying background noise, in many cases with very low signal-to-noise
ratio (SNR). In this paper, we present a method for the detection of patterns
of interest in audio signals. We propose novel trainable feature extractors,
which we call COPE (Combination of Peaks of Energy). The structure of a COPE
feature extractor is determined using a single prototype sound pattern in an
automatic configuration process, which is a type of representation learning. We
construct a set of COPE feature extractors, configured on a number of training
patterns. Then we take their responses to build feature vectors that we use in
combination with a classifier to detect and classify patterns of interest in
audio signals. We carried out experiments on four public data sets: MIVIA audio
events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that
we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on
the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund)
demonstrate the effectiveness of the proposed method and are higher than the
ones obtained by other existing approaches. The COPE feature extractors have
high robustness to variations of SNR. Real-time performance is achieved even
when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio
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