• Smart Structures and Systems
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• v.30 no.1
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• The Plant Pathology Journal
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• v.32 no.4
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• pp.300-310
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• 2016

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (${\geq}10^6cfu/ml$) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (${\Phi}PSII$) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII ($F_v/F_m$) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.263-272
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• 2016

Purpose: Pulse crop damage due to wild birds is a serious problem, to the extent that the rate of damage during the period of time between seeding and the stage of cotyledon reaches 45.4% on average. This study investigated a method of fundamentally blocking birds from eating crops by conducting vinyl mulching after seeding and identifying the growing locations for beans to perform punching. Methods: Infrared (IR) sensors that could measure the temperature without contact were used to recognize the locations of soybean cotyledons below vinyl mulch. To expand the measurable range, 10 IR sensors were arranged in a linear array. A sliding mechanical device was used to reconstruct the two-dimensional spatial variance information of targets. Spatial interpolation was applied to the two-dimensional temperature distribution information measured in real time to improve the resolution of the bean coleoptile locations. The temperature distributions above the vinyl mulch for five species of soybeans over a period of six days from the appearance of the cotyledon stage were analyzed. Results: During the experimental period, cases where bean cotyledons did and did not come into contact with the bottom of the vinyl mulch were both observed, and depended on the degree of growth of the bean cotyledons. Although the locations of bean cotyledons could be estimated through temperature distribution analyses in cases where they came into contact with the bottom of the vinyl mulch, this estimation showed somewhat large errors according to the time that had passed after the cotyledon stage. The detection results were similar for similar types of crops. Thus, this method could be applied to crops with similar growth patterns. According to the results of 360 experiments that were conducted (five species of bean ${\times}$ six days ${\times}$ four speed levels ${\times}$ three repetitions), the location detection performance had an accuracy of 36.9%, and the range of location errors was 0-4.9 cm (RMSE = 3.1 cm). During a period of 3-5 days after the cotyledon stage, the location detection performance had an accuracy of 59% (RMSE = 3.9 cm). Conclusions: In the present study, to fundamentally solve the problem of damage to beans from birds in the early stage after seeding, a working method was proposed in which punching is carried out after seeding, thereby breaking away from the existing method in which seeding is carried out after punching. Methods for the accurate detection of soybean growing locations were studied to allow punching to promote the continuous growth of soybeans that had reached the cotyledon stage. Through experiments using multiple IR sensors and a sliding mechanical device, it was found that the locations of the crop could be partially identified 3-5 days after reaching the cotyledon stage regardless of the kind of pulse crop. It can be concluded that additional studies of robust detection methods considering environmental factors and factors for crop growth are necessary.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.3
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• pp.261-273
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• 2024

This paper is an experimental study on the improvement of smoke and flame detection from different materials with YOLO. For the study, images of fires occurring in various materials were collected through an open dataset, and experiments were conducted by changing the main factors affecting the performance of the fire object detection model, such as the bounding box, polygon, and data augmentation of the collected image open dataset during data preprocessing. To evaluate the model performance, we calculated the values of precision, recall, F1Score, mAP, and FPS for each condition, and compared the performance of each model based on these values. We also analyzed the changes in model performance due to the data preprocessing method to derive the conditions that have the greatest impact on improving the performance of the fire object detection model. The experimental results showed that for the fire object detection model using the YOLOv5s6.0 model, data augmentation that can change the color of the flame, such as saturation, brightness, and exposure, is most effective in improving the performance of the fire object detection model. The real-time fire object detection model developed in this study can be applied to equipment such as existing CCTV, and it is believed that it can contribute to minimizing fire damage by enabling early detection of fires occurring in various materials.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Smart Structures and Systems
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• v.6 no.4
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• pp.405-421
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• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• Journal of Sensor Science and Technology
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• v.21 no.3
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• pp.211-216
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• 2012

Fires are the most common disaster and early fire detection is of great importance to minimize the consequent damage. Simple sensors including smoke detectors are widely used for the purpose but they are able to sense fires only at close proximity. Recently, due to the rapid advances of relevant technologies, vision-based fire sensing has attracted growing attention. In this paper, a novel visual sensing technique to automatically detect fire is presented. The proposed technique consists of multiple steps of image processing: pixel-level, block-level, and frame level. At the first step, fire flame pixel candidates are selected based on their color values in YIQ space from the image of a camera which is installed as a vision sensor at a fire scene. At the second step, the dynamic parts of flames are extracted by comparing two consecutive images. These parts are then represented in regularly divided image blocks to reduce pixel-level detection error and simplify following processing. Finally, the temporal change of the detected blocks is analyzed to confirm the spread of fire. The proposed technique was tested using real fire images and it worked quite reliably.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.287-293
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• 2014

Early fire detection is very important to avoid loss of lives and material damage. The conventional smoke detector sensors have difficulties in detecting smoke in large outdoor areas. The video-based smoke detection can overcome these drawbacks. This paper proposes a new smoke detection method in video sequences. It uses the ratio of variation rate of subband energy in the wavelet transform domain. In order to reduce the false alarm, candidate smoke blocks are detected by using motion, decrease of chromaticity and the average intensity of block in the YUV color space. Finally, it decides whether the candidate smoke blocks are smokes or not by using their temporal changes of subband energies in the wavelet transform domain. Experimental results show that the proposed method noticeably increases the accuracy of smoke detection and reduces false alarm compared with the conventional smoke detection methods using wavelets.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.249-255
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• 2022

Alzheimer's disease (AD) represents a major public health concern and has been identified as a research priority. Clinical research evidence supports that the core cerebrospinal fluid (CSF) biomarkers for AD, including amyloid-β (Aβ42), total tau (T-tau), and phosphorylated tau (P-tau), reflect key elements of AD pathophysiology. Nevertheless, advances in the clinical identification of new indicators will be critical not only for the discovery of sensitive, specific, and reliable biomarkers of preclinical AD pathology, but also for the development of tests that facilitate the early detection and differential diagnosis of dementia and disease progression monitoring. The early detection of AD in its presymptomatic stages would represent a great opportunity for earlier therapeutic intervention. The chance of successful treatment would be increased since interventions would be performed before extensive synaptic damage and neuronal loss would have occurred. In this study, the importance of developing an early diagnostic method using cognitive decline biomarkers that can discriminate between normal, mild cognitive impairment (MCI), and AD preclinical stages has been emphasized.