• Smart Structures and Systems
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• 제30권1호
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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.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• Journal of information and communication convergence engineering
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• 제7권3호
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• pp.258-262
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• 2009

A Practical application of environmental monitoring system based on wireless sensor node network with the core of embedded system STR711FR2 microprocessor is presented in the paper. The adaptable and classifiable wireless sensor node network is used to achieve the data acquisition and multi-hop wireless communication of parameters of the monitoring base station environment including repeaters. The structure of the system is proposed and the hardware architecture of the system is designed, and the system operating procedures is proposed. As a result of field test, designed hardware platform operated with 50kbps bit rate and 5MHz channel spacing at 2040Hz. The wireless monitoring system can be managed and swiftly retreated without support of base station environmental monitoring.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Smart Structures and Systems
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• 제15권6호
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• 한국자동차공학회논문집
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• 제13권4호
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• pp.97-104
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• 2005

Vision-based lane sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system is developed with considering sensor configuration aspects. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3-dimensional road geometry can be reconstructed in a robust manner. A new monitoring model for estimating the road geometry parameters is constructed to reduce the number of the measured signals. The selection of the sensor configuration and specifications is investigated by utilizing the characteristics of standard highways. Based on the sensor configurations, it is shown that appropriate sensing region on the camera image coordinate can be determined. The proposed system is implemented on a passenger car and verified experimentally.

• Smart Structures and Systems
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• 제32권5호
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• 한국정보전자통신기술학회논문지
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• 제10권2호
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• pp.133-140
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• 2017

본 논문은 컴퓨터 비전 기술을 기반으로 자동 차량 감시 시스템을 제안하였다. 실시간 주행표시 시스템은 ITS의 필수 요건을 충족하면서, 자동 감시제어가 가능한 시스템이다. 이러한 장점은 확실한 자동차 추적에 대해 주요 장애물 처리 시스템 적용할 경우, 움직이는 물체에 대한 그림자 추적이다. 추적 차량 이미지에서 모든 종류의 정보를 획득하기 위해 차량을 확실하게 감시 화면에 나타나게 하였다. 시간이 지남에 따라 차량을 정밀 추적 제어 할 필요가 있고, 입체 모델 기반접근 방식 또한 필요한 방식으로 적용하였다. 일반적으로 개체 또는 와이어 프레임 모델의 골격에 의해 차량의 각각의 유형을 나타내었고, 시스템이 실시간 실행되지 않더라도 차량 궤적은 3D기반 방식에서 높은 정밀도로 측정 될 수 있다는 점을 보여 준다. 본 논문에서는 차량, 배경, 그림자에 적용 가능하고, 도로 교통 감시의 시스템을 분할 방법을 역시 적용하였다. 과속 자동차의 속도 추적을 통해 낮은 레벨의 차량제어 추적기의 유효성 역시 실행 하였다. 결론에서 개발된 추적 제어 시스템에서 향상된 자동차 추적의 방법을 개선하고자 하였으며 고속도로 감시제어 시스템을 개발하고자 하였다.

• 한국구조물진단유지관리공학회지
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• 제18권2호
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• pp.10-16
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• 2014

• 한국정보과학회논문지:시스템및이론
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• 제30권11호
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• pp.610-620
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• 2003

다양한 그림자는 비젼 기반 차량 검출에서 오류를 발생시키는 주요 원인이다. 본 논문에서는 노면 표시 기반 방법과 배경 빼기 및 에지(BS & Edge) 방법이라는 두 가지 방안을 차량 검출과 그림자 제거를 위해 제안하였다. 노변의 지형 지물들로 인해서 발생하는 그림자의 영향이 크게 증가하는 상황에서의 실험을 통해서 96% 이상의 차량 검출 정확도를 나타냄을 확인하였다. 전술한 두 가지 방법을 기반으로 하여, 차량 추적, 차량 계수, 차종 분류, 그리고 속도 측정을 수행하여 각 차로의 부하를 나타내는 데 사용되는 차량 흐름과 관련된 여러 가지 교통 파라미터를 추출하였다.