• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.623-627
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• 2021

Cervical foraminal stenosis is a disease in which the nerves that pass from the spinal canal to the limbs are narrowed and the nerves are compressed or damaged. Due to the lack of an imaging method that provides quantitatively stenosis, this study attempted to evaluate the area of the cervical vertebrae by reconstructing a three-dimensional computed tomography image, and to determine the area of the neural foramen in normal adults to calculate the stenosis rate. Using a three-dimensional image processing program, the surrounding bones including the posterior spinous process, lateral process, and lamellar bones of the cervical vertebra were removed so that the neural foramen could be observed well. A region of interest including the neural foraminal area of the three-dimensional image was set using ImageJ, and the number of pixels in the neural foraminal area was measured. The neural foraminal area was calculated by multiplying the number of measured pixels by the pixel size. To measure the largest neural foraminal area, it was measured between 40~50 degrees in the opposite direction and 15~20 degrees toward the head. The average area of the right C2-3 foramen was 44.32 mm², C3-4 area was 34.69 mm², C4-5 area was 36.41 mm², C5-6 area was 35.22 mm², C6-7 area was 36.03 mm². The average area of the left C2-3 foramen was 42.71 mm², C3-4 area was 32.23 mm², C5-6 area was 34.56 mm², and C6-7 area was 31.89 mm². By creating a reference table based on the neural foramen area of normal adults, the stenosis rate of patients with neural foraminal stenosis could be quantitatively calculated. It is expected that this method can be used as basic data for the diagnosis of cervical vertebral foraminal stenosis.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• Journal of radiological science and technology
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• v.37 no.2
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• pp.93-100
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• 2014

Recently the development of portable digital wireless imaging system, which acquires digital radiation images by using wireless LAN telecommunications function in an easy and fast way, provides lots of convenience for people. Considering the characteristics of portable imaging tests on emergency and critical patients, this study aims to suggest guidelines for Digital wireless detector by evaluating the effect of de-centering of focus-grid and displacement of subject in detector on the quality of image. The equipments used for this study were Elmo-T6 Digital Mobile X-ray system (SIMAZU Corp.), el' Tor ($14{\times}17$ "Wireless detector), Grid (10:1) and Chest & head phantom. After acquiring post-processing image according to dose increase and de-centering image of grid-focus and head phantom displacement image, this study compared, analyzed and evaluated these images by using a digital image analysis program by Image J. In the change of images based on dose increase, images were rough in the dose of 0.5 mAs, while there was no difference among images in the proper dose of 1~2 mAs and, especially from 2.5 mAs, average value of pixels radically decreased, affecting contrast. Over 3 mAs, contrast dropped due to saturation phenomenon of lungs. As the result of analysis using Image J program, with the increase of displacement between focus-grid and head phantom, the frequency of low pixel value also increase, causing the outline of surface image to disappear, which in turn affects contrast. For better quality imaging, a radiographer must be aware before the time of test that the image quality can be changed based on the critical patient's posture, movement, respiration, displacement of X-ray tube and distance of imaging.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.