This study has suggested an image analysis system based on the Deep Learning for CCTV pedestrian detection and tracing improvement and did experiments for objective verification by designing study model and evaluation model. The study suggestion is that if someone’s face did not be recognized in crime scene CCTV footage, the same pedestrian would be traced and found in other image data from other CCTV by using Color Intensity Classification method for clothes colors as body features and body fragmentation technique into 7 parts (2 arms, 2 legs, 1 body, 1 head, and 1 total). If one of other CCTV footage has recorded its face, the identity of the person would be secured. It is not only detection but also search from stored bulk storage to prevent accidents or cope with them in advance by cost reduction of manpower and a fast response. Therefore, CIC7P(Color Intensity Classification 7 Part Base Model) had been suggested by learning device such as Machine Learning or Deep Learning to improve accuracy and speed for pedestrian detection and tracing. In addition, the study has proved that it is an advanced technique in the area of pedestrian detection through experimental proof.

PURPOSES :This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors.METHODS :Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling.RESULTS :The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables.CONCLUSIONS :Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

Deep learning techniques are being studied and developed throughout the medical, agricultural, aviation, and automotive industries. It can be applied to construction fields such as concrete cracks and welding defects. One of the best performing techniques of deep running is CNN technique. In this study, we analyzed the classification of handwritten images using CNN technique before applying them to construction field. Deep running is generally more accurate with deeper layers, but analysis cost is high. In addition, many variations can occur depending on training options. Therefore, this study performed a parametric study to be a reference when CNN technique was applied through accuracy analysis according to training options.

Noh, Hyung-nam. 2017. “Entertainment Science Based on Deep Learning: focused on Areal Sociolinguistics”. The Sociolinguistic Journal of Korea 25(1). 27~52. The aim of this paper is to suggest a new scientific discipline in sociolinguistic research, dealing with entertainment science based on deep learning focused on areal sociolinguistics as a current methodology de facto made by ultra-fusion of area studies and sociolinguistics. From a fact-oriented and data-oriented analysis perspective this paper examines real phenomena of areal sociolinguistics provoked by two famous sing-a-song writers: America’s Robert Allen Zimmerman, so-called 2016 Nobel prize winner Bob Dylan, and Brazil’s Paulo Coelho de Souza. The results of the qualitative analyses between two eminent areas, where particular attributes of alternative societies are filled with swarm intelligence on the basis of resistance consciousness, suggest the areal sociolinguistics mentioned-above. From the diachronic and synchronic viewpoints of cross-over geographical cultures this paper makes a mid-range generalization, on making a definition about alternative societies in America and Brazil in spite of the geographical methodology of area studies between the two countries, being offered by stubborn resistance against ready-made ideas to calm down keen psychological conflicts among established moral principles to overcome philosophical catastrophe in social chaos, and full of competitive instinct against existing generations.

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

본 연구에서는 신속하고 편리한 고속도로 유지관리를 위해 주행 중인 차량에서 실시간으로 영상을 수집하고 분석하여 현장의 노면 포장 상태를 모니터링하고 손상을 탐지하는 딥러닝 기반의 고속도로 포장 손상 조사 기술에 대해 소개한다.

딥러닝 모델은 주어진 학습용 데이터에서 탐지하고자 하는 물체의 특징을 추출하기 때문에, 딥러닝 모델 학습을 위한 학습용 데이터 구축은 매우 중요하다. 본 연구에서는 균열을 탐지하는 딥러닝 모델의 성능을 향상시키기 위해, 실제 콘크리트 구조물이나 아스팔트 도로 표면에서 자주 발견될 수 있는 나뭇가지, 거미줄, 전선 등을 학습 데이터에 자동으로 포함시키고, negative 영역으로 분류하는 알고리즘을 개발하였다. 제안된 알고리즘을 사용하여 학습된 딥러닝 모델을 실제 도로 표면에 발생한 균열 탐지에 적용하여 실제 균열 탐지에 사용될 수 있음을 보였다.

최근 사회기반시설(SOC)의 증가와 노후화에 따라 기존의 인력중심의 육안검사를 기반으로 한 안전점검은 경제성과 안전성, 효율성 면에서 한계를 가지고 있다. 본 연구에서는 육안점검의 한계를 개선하기 위해 딥러닝 모델 기반 물체를 탐지하는 기술을 활용하여 터널 콘크리트 균열을 자동으로 탐지하는 기술을 개발하였으며, 이를 실제 터널 영상에 적용하여 그 성능을 검증하였다.

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in ‘tfrecords’ format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.

Last few years, many researches on deep learning-based crack detection model have been reported in order to develop an efficient structure inspection method. While developing crack detection deep learning model, many research results reported the importance of the training data. Since most of the research results only qualitatively discussed the importance of training data, this study examine the influence of the training data by experiment, especially in the case of negative samples such as construction joint, spider web and concrete blocks.

Construction safety is one of the significant problems on the world. Deep learning is an emerging term that acquires, processes and analyses image or video data to help computers have a high-level visual understanding of the world. In recent years, it has been introduced into the construction industry for improvements of occupational health and safety. This research contributes in solving this problem by using deep learning only RGB images that output detects the hazard zone on construction sites. The main goal of this study is to use different computer vision and deep learning to develop for different cases concerning fall related hazards.

The damage detection method of blade systems largely depends on the personal ability of an inspector using a camera. Thus, this paper proposes a deep learning-based detection method that can rapidly and reliably identify and evaluate the damages on the blades.