25 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
A deep learning approach to crack detection on road surfaces
Currently, modern achievements in the field of deep learning are increasingly being applied in practice. One of the practical uses of deep learning is to detect cracks on the surface of the roadway. The destruction of the roadway is the result of various factors: for example, the use of low-quality material, non-compliance with the standards of laying asphalt, external physical impact, etc. Detection of these damages in automatic mode with high speed and accuracy is an important and complex task. An effective solution to this problem can reduce the time of services that carry out the detection of damage and also increase the safety of road users. The main challenge for automatically detecting such damage, in most cases, is the complex structure of the roadway. To accurately detect this damage, we use U-Net. After that we improve the binary map with localized cracks from the U-Net neural network, using the morphological filtering. This solution allows localizing cracks with higher accuracy in comparison with traditional methods crack detection, as well as modern methods of deep learning. All experiments were performed using the publicly available CRACK500 dataset with examples of cracks and their binary maps
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network
Automated pavement crack image segmentation is challenging because of
inherent irregular patterns, lighting conditions, and noise in images.
Conventional approaches require a substantial amount of feature engineering to
differentiate crack regions from non-affected regions. In this paper, we
propose a deep learning technique based on a convolutional neural network to
perform segmentation tasks on pavement crack images. Our approach requires
minimal feature engineering compared to other machine learning techniques. We
propose a U-Net-based network architecture in which we replace the encoder with
a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule
based on cyclical learning rates to speed up the convergence. Our method
achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset,
outperforming other algorithms tested on these datasets. We perform ablation
studies on various techniques that helped us get marginal performance boosts,
i.e., the addition of spatial and channel squeeze and excitation (SCSE)
modules, training with gradually increasing image sizes, and training various
neural network layers with different learning rates.Comment: Accepted for publication in IEEE Acces
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies
Detecting subtle defects in window frames, including dents and scratches, is
vital for upholding product integrity and sustaining a positive brand
perception. Conventional machine vision systems often struggle to identify
these defects in challenging environments like construction sites. In contrast,
modern vision systems leveraging machine and deep learning (DL) are emerging as
potent tools, particularly for cosmetic inspections. However, the promise of DL
is yet to be fully realized. A few manufacturers have established a clear
strategy for AI integration in quality inspection, hindered mainly by issues
like scarce clean datasets and environmental changes that compromise model
accuracy. Addressing these challenges, our study presents an innovative
approach that amplifies defect detection in DL models, even with constrained
data resources. The paper proposes a new defect detection pipeline called
InspectNet (IPT-enhanced UNET) that includes the best combination of image
enhancement and augmentation techniques for pre-processing the dataset and a
Unet model tuned for window frame defect detection and segmentation.
Experiments were carried out using a Spot Robot doing window frame inspections
. 16 variations of the dataset were constructed using different image
augmentation settings. Results of the experiments revealed that, on average,
across all proposed evaluation measures, Unet outperformed all other algorithms
when IPT-enhanced augmentations were applied. In particular, when using the
best dataset, the average Intersection over Union (IoU) values achieved were
IPT-enhanced Unet, reaching 0.91 of mIoU
Auscultación de pavimentos mediante interpretación de imágenes
Este documento describe el diseño e implementación de un sistema para realizar la auscultación de pavimentos rÃgidos por medio de tratamiento de imágenes. La auscultación de pavimentos es un proceso que sirve para evaluar el estado actual del pavimento, brindando una herramienta de apoyo que permita determinar cuándo realizar un mantenimiento preventivo.Pregrad