• Title/Summary/Keyword: Deep Learning System

Search Result 1,738, Processing Time 0.04 seconds

Design of Smart Glasses Platform walking guide for the visually impaired (시각장애인을 위한 보행 안내 스마트 안경 플랫폼 설계)

  • Lee, Jaebeom;Jang, Jongwook;Jang, Sungjin
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2021.10a
    • /
    • pp.320-322
    • /
    • 2021
  • As the world's elderly population increases, the proportion of visually impaired is also increasing, and there are still many restrictions on the use of outside activities, such as safety problems and lack of guidance information. To solve this problem, research on smart devices such as smart glasses with optical character recognition (OCR) function is being actively conducted. In this paper, we propose a system that recognizes obstacles ahead and informs information by voice, and also guides the way to the destination. Using the deep learning object recognition model Yolo, it let them to recognize the risk factors as obstacles such as stairs and Larva cones. and it also deliver the information with a voice. so you can expect that the visually impaired can do a lot of different activity even more now that system takes the visually impaired to the destination by using the directions API, voice recognition, TTS library.

  • PDF

Design and Implementation of Dangerous of Image Recognition based Cup Contamination Measurement System (이미지 인식 기반의 컵 오염 여부 측정 시스템의 설계 및 구현)

  • Lee, Taejun;Chae, Heeseok;Lee, Sangwon;Kim, Jaemin;Jung, Hoekyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2022.10a
    • /
    • pp.213-215
    • /
    • 2022
  • Recently, deep learning technology that processes images has been widely used in fire detection, autonomous driving, and defective product detection. In particular, in order to determine whether a product is contaminated or not, it can be identified through the contaminants passed from the existing sensor data, but technologies for recognizing cracks in products or contaminants themselves as images are being actively studied in various fields. In this paper, a system for classifying uncontaminated normal cups and contaminated cups through images was designed and implemented. The image was analyzed using an open image and a photographed image, and the image was analyzed by extracting the upper part of the cup image using Google Objectron for 3D object recognition. Through this study, it is thought that it will be used in various ways for research that can extract the contamination level of products required in the hygiene field based on images.

  • PDF

Correlation Extraction from KOSHA to enable the Development of Computer Vision based Risks Recognition System

  • Khan, Numan;Kim, Youjin;Lee, Doyeop;Tran, Si Van-Tien;Park, Chansik
    • International conference on construction engineering and project management
    • /
    • 2020.12a
    • /
    • pp.87-95
    • /
    • 2020
  • Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

  • PDF

Determination of a priority for leakage restoration considering the scale of damage in for water distribution systems (피해규모를 고려한 용수공급시스템 누수복구 우선순위 선정)

  • Kim, Ryul;Kwon, Hui Geun;Choi, Young Hwan
    • Journal of Korea Water Resources Association
    • /
    • v.56 no.10
    • /
    • pp.679-690
    • /
    • 2023
  • Leakage is one of the representative abnormal conditions in Water distribution systems (WDSs). Leakage can potentially occur and cause immediate economic and hydraulic damage upon occurrence. Therefore, leakage detection is essential, but WDSs are located underground, it is difficult. Moreover, when multiple leakage occurs, it is required to prioritize restoration according to the scale and location of the leakage, applying for an optimal restoration framework can be advantageous in terms of system resilience. In this study, various leakage scenarios were generated based on the WDSs hydraulic model, and leakage detection was carried out containing location and scale using a Deep learning-based model. Finally, the leakage location and scale obtained from the detection results were used as a factor for the priority of leakage restoration, and the results of the priority of leakage restoration were derived. The priority of leakage restoration considered not only hydraulic factors but also socio-economic factors (e.g., leakage scale, important facilities).

Nanotechnology in early diagnosis of gastro intestinal cancer surgery through CNN and ANN-extreme gradient boosting

  • Y. Wenjing;T. Yuhan;Y. Zhiang;T. Shanhui;L. Shijun;M. Sharaf
    • Advances in nano research
    • /
    • v.15 no.5
    • /
    • pp.451-466
    • /
    • 2023
  • Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

Environmental Monitoring and Forecasting Using Advanced Remote Sensing Approaches (최신 원격탐사 기법을 이용한 지구환경 모니터링 및 예측)

  • Seonyoung Park;Ahram Song;Yangwon Lee;Jungho Im
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.5_3
    • /
    • pp.885-890
    • /
    • 2023
  • As satellite technology progresses, a growing number of satellites-like CubeSat and radar satellites-are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trendsindicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in thisfield. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth's environment. The objective is to provide direction for the future of remote sensing research.

Abnormal Flight Detection Technique of UAV based on U-Net (U-Net을 이용한 무인항공기 비정상 비행 탐지 기법 연구)

  • Myeong Jae Song;Eun Ju Choi;Byoung Soo Kim;Yong Ho Moon
    • Journal of Aerospace System Engineering
    • /
    • v.18 no.3
    • /
    • pp.41-47
    • /
    • 2024
  • Recently, as the practical application and commercialization of unmanned aerial vehicles (UAVs) is pursued, interest in ensuring the safety of the UAV is increasing. Because UAV accidents can result in property damage and loss of life, it is important to develop technology to prevent accidents. For this reason, a technique to detect the abnormal flight state of UAVs has been developed based on the AutoEncoder model. However, the existing detection technique is limited in terms of performance and real-time processing. In this paper, we propose a U-Net based abnormal flight detection technique. In the proposed technique, abnormal flight is detected based on the increasing rate of Mahalanobis distance for the reconstruction error obtained from the U-Net model. Through simulation experiments, it can be shown that the proposed detection technique has superior detection performance compared to the existing detection technique, and can operate in real-time in an on-board environment.

An User-Friendly Kiosk System Based on Deep Learning (딥러닝 기반 사용자 친화형 키오스크 시스템)

  • Su Yeon Kang;Yu Jin Lee;Hyun Ah Jung;Seung A Cho;Hyung Gyu Lee
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.29 no.1
    • /
    • pp.1-13
    • /
    • 2024
  • This study aims to provide a customized dynamic kiosk screen that considers user characteristics to cope with changes caused by increased use of kiosks. In order to optimize the screen composition according to the characteristics of the digital vulnerable group such as the visually impaired, the elderly, children, and wheelchair users, etc., users are classified into nine categories based on real-time analysis of user characteristics (wheelchair use, visual impairment, age, etc.). The kiosk screen is dynamically adjusted according to the characteristics of the user to provide efficient services. This study shows that the system communication and operation were performed in the embedded environment, and the used object detection, gait recognition, and speech recognition technologies showed accuracy of 74%, 98.9%, and 96%, respectively. The proposed technology was verified for its effectiveness by implementing a prototype, and through this, this study showed the possibility of reducing the digital gap and providing user-friendly "barrier-free kiosk" services.

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography

  • Si Eun Lee;Hanpyo Hong;Eun-Kyung Kim
    • Korean Journal of Radiology
    • /
    • v.25 no.4
    • /
    • pp.343-350
    • /
    • 2024
  • Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

Real-time Steel Surface Defects Detection Appliocation based on Yolov4 Model and Transfer Learning (Yolov4와 전이학습을 기반으로한 실시간 철강 표면 결함 검출 연구)

  • Bok-Kyeong Kim;Jun-Hee Bae;NGUYEN VIET HOAN;Yong-Eun Lee;Young Seok Ock
    • The Journal of Bigdata
    • /
    • v.7 no.2
    • /
    • pp.31-41
    • /
    • 2022
  • Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.