A Study on Low-Light Image Enhancement Technique for Improvement of Object Detection Accuracy in Construction Site (건설현장 내 객체검출 정확도 향상을 위한 저조도 영상 강화 기법에 관한 연구)
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- Tunnel and Underground Space
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- v.34 no.3
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- pp.208-217
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- 2024
There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.
Currently, it is almost impossible to move the focus of a previously taken photo to a different location. This paper challenges a technology that can move the focus of a captured photo to another location after shooting. To achieve this goal, this paper proposed and implemented a method for taking photos with various focuses at the moment the camera took pictures and storing them in a single JPEG file to extract photos focused on the user's preferred location. In this paper, two methods are implemented: taking various photos by quickly moving the focal length of the lens from close to far away, and taking various photos focused on each object by recognizing objects in the camera viewfinder. Various photos taken are stored in a single JPEG to maintain compatibility with traditional photo viewers. At this time, this JPEG file used the All-in-JPEG format proposed in previous research to store a variety of images. This paper verified its practicality by implementing these technologies in an Android app named OnePIC.
The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.
Aquaculture has historically delivered marine products because the country is surrounded by ocean on three sides. Surveys on production have been conducted recently to systematically manage aquaculture facilities. Based on survey results, pricing controls on marine products has been implemented to stabilize local fishery resources and to ensure minimum income for fishermen. Such surveys on aquaculture facilities depend on manual digitization of aerial photographs each year. These surveys that incorporate manual digitization using high-resolution aerial photographs can accurately evaluate aquaculture with the knowledge of experts, who are aware of each aquaculture facility's characteristics and deployment of those facilities. However, using aerial photographs has monetary and time limitations for monitoring aquaculture resources with different life cycles, and also requires a number of experts. Therefore, in this study, we investigated an automatic prototype system for detecting boundary information and monitoring aquaculture facilities based on satellite images. KOMPSAT-3 (13 Scene), a local high-resolution satellite provided the satellite imagery collected between October and April, a time period in which many aquaculture facilities were operating. The ANN classification method was used for automatic detecting such as cage, longline and buoy type. Furthermore, shape files were generated using a digitizing image processing method that incorporates polygon generation techniques. In this study, our newly developed prototype method detected aquaculture facilities at a rate of 93%. The suggested method overcomes the limits of existing monitoring method using aerial photographs, but also assists experts in detecting aquaculture facilities. Aquaculture facility detection systems must be developed in the future through application of image processing techniques and classification of aquaculture facilities. Such systems will assist in related decision-making through aquaculture facility monitoring.
Purpose: To evaluate kidney function, renal relative uptake is very important and is affected by kidney and the setting of background region of interest (ROI). In particular, in the case of patients with hydronephrosis to the naked eyes, such as size, position and shape etc. can be difficult to identify. So according to ROI to be set by user, the results are many differences. This study assumes the ROI of a constant kidney. According to the change of background ROI by analyzing renal relative uptake affect how the results are intended to study. Materials and Methods: From January 2012 to February 2013, we analyzed 27 patients with hydronephrosis who were examined MAG3 test in nuclear medicine department of Samsung medical center. After patients were received intravenous injection of
Tooth implant is located in oral cavity and affects neck, skull base, and facail image. These magnetic inhomogeneities are usually frequency encoding direction which cause artifacts due to change of signal strength and geometric distortion. First, to evaluate signal to noise ratio (SNR) of magnetic resonance image caused by tooth implant this study uses meat phantom which is similar to human body and is consisted with fat, muscle, and water to measure signal to noise ratio. Second, signal to noise ratio by using custom-made fixed phantom is measured, and then signal to noise ratio size of different tooth implant types is compared and analyzed. The measured signal to noise ratio values of Brushite, HSA, Metal, and RBM for meat phantom were 2.76, 2.22, 1.88, and 1.57 on T1 SE, 1.88, 1.78, 1.65, and 1.79 on T2 FLAIR, 2.28, 2.25, 2.88, and 2.05 on T2 FSE, and 2.74, 1.94, 1.67, and 1.48 on T2 GRE. The measured signal to noise ratio values of Brushite, HSA, Metal, and RBM for fixed water phantom were 1.2, 1.06, 1.12, and 1.22 on DWI, 1.93, 1.87, 1.93, and 2.06 T1 SE, 1.83, 1.76, 1.82, and 1.92 on T2 FLAIR, 1.85, 1.79, 7.86, and 1.97 on T2 FSE, and 1.97, 1.93, 1.99, and 2.06 on T2 GRE. By considering through the results, patients and dentists need to consider some impacts from testing many aspects although their main purpose of having tooth implants is a dental restoration. Moreover, depending on the tooth implant characteristics of individual patients this study results can be used as baseline data when choosing test protocol.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
Purpose The lung segment ratio which is obtained through quantitative analyses of lung perfusion scan images is calculated to evaluate the lung function pre and post surgery. In this Study, the planar image production methods by using Q-Metrix (GE Healthcare, USA) program capable of not only quantitative analysis but also computation of the segment ratio after having performed SPECT/CT are comparatively evaluated. Materials and Methods Lung perfusion scan and SPECT/CT were performed on 50 lung cancer patients prior to surgery who visited our hospital from May 1, 2015 to September 13, 2016 by using Discovery 670(GE Healthcare, USA) equipment. AP(Anterior Posterior)method that uses planar image divided the frontal and rear images into three rectangular portions by means of ROI tool while PO(Posterior Oblique)method computed the segment ratio by dividing the right lobe into three parts and the left lobe into two parts on the oblique image. Segment ratio was computed by setting the ROI and VOI in the CT image by using Q-Metrix program and statistically analysis was performed with SPSS Ver. 23. Results Regarding the correlation concordance rate of Q-Metrix and AP methods, RUL(Right upper lobe), RML(Right middle lobe) and RLL(Right lower lobe) were 0.224, 0.035 and 0.447. LUL(Left upper lobe) and LLL(Left lower lobe) were found to be 0.643 and 0.456, respectively. In the PO method, the right lobe were 0.663, 0.623 and 0.702, respectively, while the left lobe were 0.754 and 0.823. When comparison was made by using the Paired sample T-test, Right lobe were