• 제목/요약/키워드: Object Augmentation

검색결과 93건 처리시간 0.025초

Object-Transportation Control of Cooperative AGV Systems Based on Virtual-Passivity Decentralized Control Algorithm

  • Suh, Jin-Ho;Lee, Young-Jin;Lee, Kwon-Soon
    • Journal of Mechanical Science and Technology
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    • 제19권9호
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    • pp.1720-1730
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    • 2005
  • Automatic guided vehicle in the factory has an important role to advance the flexible manufacturing system. In this paper, we propose a novel object-transportation control algorithm of cooperative AGV systems to apply decentralized control to multiple AGV systems. Each AGV system is under nonholonomic constraints and conveys a common object-transportation in a horizontal plain. Moreover it is shown that cooperative robot systems ensure stability and the velocities of augmented systems convergence to a scaled multiple of each desired velocity field for cooperative AGV systems. Finally, the application of proposed virtual passivity-based decentralized control algorithm via system augmentation is applied to trace a circle. Finally, the simulation and experimental results for the object-transportation by two AGV systems illustrates the validity of the proposed virtual-passivity decentralized control algorithm.

Video Augmentation by Image-based Rendering

  • Seo, Yong-Duek;Kim, Seung-Jin;Sang, Hong-Ki
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송공학회 1998년도 Proceedings of International Workshop on Advanced Image Technology
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    • pp.147-153
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    • 1998
  • This paper provides a method for video augmentation using image interpolation. In computer graphics or augmented reality, 3D information of a model object is necessary to generate 2D views of the model, which are then inserted into or overlayed on environmental views or real video frames. However, we do not require any three dimensional model but images of the model object at some locations to render views according to the motion of video camera which is calculated by an SFM algorithm using point matches under weak-perspective (scaled-orthographic) projection model. Thus, a linear view interpolation algorithm is applied rather than a 3D ray-tracing method to get a view of the model at different viewpoints from model views. In order to get novel views in a way that agrees with the camera motion the camera coordinate system is embedded into model coordinate system at initialization time on the basis of 3D information recovered from video images and model views, respectively. During the sequence, motion parameters from video frames are used to compute interpolation parameters, and rendered model views are overlayed on corresponding video frames. Experimental results for real video frames and model views are given. Finally, discussion on the limitations of the method and subjects for future research are provided.

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Human Detection using Real-virtual Augmented Dataset

  • Jongmin, Lee;Yongwan, Kim;Jinsung, Choi;Ki-Hong, Kim;Daehwan, Kim
    • Journal of information and communication convergence engineering
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    • 제21권1호
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    • pp.98-102
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    • 2023
  • This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

Subsurface anomaly detection utilizing synthetic GPR images and deep learning model

  • Ahmad Abdelmawla;Shihan Ma;Jidong J. Yang;S. Sonny Kim
    • Geomechanics and Engineering
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    • 제33권2호
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    • pp.203-209
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    • 2023
  • One major advantage of ground penetrating radar (GPR) over other field test methods is its ability to obtain subsurface images of roads in an efficient and non-intrusive manner. Not only can the strata of pavement structure be retrieved from the GPR scan images, but also various irregularities, such as cracks and internal cavities. This article introduces a deep learning-based approach, focusing on detecting subsurface cracks by recognizing their distinctive hyperbolic signatures in the GPR scan images. Given the limited road sections that contain target features, two data augmentation methods, i.e., feature insertion and generation, are implemented, resulting in 9,174 GPR scan images. One of the most popular real-time object detection models, You Only Learn One Representation (YOLOR), is trained for detecting the target features for two types of subsurface cracks: bottom cracks and full cracks from the GPR scan images. The former represents partial cracks initiated from the bottom of the asphalt layer or base layers, while the latter includes extended cracks that penetrate these layers. Our experiments show the test average precisions of 0.769, 0.803 and 0.735 for all cracks, bottom cracks, and full cracks, respectively. This demonstrates the practicality of deep learning-based methods in detecting subsurface cracks from GPR scan images.

다중영상을 이용한 딥러닝 기반 온디바이스 증강현실 시스템 (Deep Learning Based On-Device Augmented Reality System using Multiple Images)

  • 정태현;박인규
    • 방송공학회논문지
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    • 제27권3호
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    • pp.341-350
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    • 2022
  • 본 논문은 온디바이스 환경에서 다중 시점 영상을 입력 받아 객체를 증강하고, 현실 공간에 의한 가려짐을 구현하는 딥러닝 기반의 증강현실 시스템을 제안한다. 이는 세부적으로 카메라 자세 추정, 깊이 추정, 객체 증강 구현의 세 기술적 단계로 나눠지며 각 기법은 온디바이스 환경에서의 최적화를 위해 다양한 모바일 프레임워크를 사용한다. 카메라 자세 추정 단계에서는 많은 계산량을 필요로 하는 특징 추출 알고리즘을 GPU 병렬처리 프레임워크인 OpenCL을 통해 가속하여 사용하며, 깊이 영상 추론 단계에서는 모바일 심층신경망 프레임워크 TensorFlow Lite를 사용하여 가속화된 단안, 다중 영상 기반의 깊이 영상 추론을 수행한다. 마지막으로 모바일 그래픽스 프레임워크 OpenGL ES를 활용해 객체 증강 및 가려짐을 구현한다. 제시하는 증강현실 시스템은 안드로이드 환경에서 GUI를 갖춘 애플리케이션으로 구현되며 모바일과 PC 환경에서의 동작 정확도 및 처리 시간을 평가한다.

점운증강을 위한 프로젝션 손실 (Projection Loss for Point Cloud Augmentation)

  • 오신모;이효종
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2019년도 춘계학술발표대회
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    • pp.482-484
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    • 2019
  • Learning and analyzing 3D point clouds with deep networks is challenging due to the limited and irregularity of the data. In this paper, we present a data-driven point cloud augmentation technique. The key idea is to learn multilevel features per point and to reconstruct to a similar point set. Our network is applied to a projection loss function that encourages the predicted points to remain on the geometric shapes with a particular target. We conduct various experiments using ShapeNet part data to evaluate our method and demonstrate its possibility. Results show that our generated points have a similar shape and are located closer to the object.

Keypoint Detection과 Annoy Tree를 사용한 2D Hand Pose Estimation (Fast Hand Pose Estimation with Keypoint Detection and Annoy Tree)

  • 이희재;강민혜
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
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    • pp.277-278
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    • 2021
  • 최근 손동작 인식에 대한 연구들이 활발하다. 하지만 대부분 Depth 정보를 포함한3D 정보를 필요로 한다. 이는 기존 연구들이 Depth 카메라 없이는 동작하지 않는다는 한계점이 있다는 것을 의미한다. 본 프로젝트는 Depth 카메라를 사용하지 않고 2D 이미지에서 Hand Keypoint Detection을 통해 손동작 인식을 하는 방법론을 제안한다. 학습 데이터 셋으로 Facebook에서 제공하는 InterHand2.6M 데이터셋[1]을 사용한다. 제안 방법은 크게 두 단계로 진행된다. 첫째로, Object Detection으로 Hand Detection을 수행한다. 데이터 셋이 어두운 배경에서 촬영되어 실 사용 환경에서 Detection 성능이 나오지 않는 점을 해결하기 위한 이미지 합성 Augmentation 기법을 제안한다. 둘째로, Keypoint Detection으로 21개의 Hand Keypoint들을 얻는다. 실험을 통해 유의미한 벡터들을 생성한 뒤 Annoy (Approximate nearest neighbors Oh Yeah) Tree를 생성한다. 생성된 Annoy Tree들로 후처리 작업을 거친 뒤 최종 Pose Estimation을 완료한다. Annoy Tree를 사용한 Pose Estimation에서는 NN(Neural Network)을 사용한 것보다 빠르며 동등한 성능을 냈다.

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분산 수동속도장 제어법을 이용한 다중 AGV 시스템의 협조 이송제어 (A Cooperative Object-Transportation Control of Multiple AGV Systems using Decentralized Passive Velocity Field Control Algorithm)

  • 서진호;김영복;이권순
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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    • pp.391-393
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    • 2005
  • Automatic guided vehicle(AGV) in the factory has an important role to advance the flexible manufacturing system. In this paper, we propose a novel object-transportation control algorithm of cooperative AGV systems to apply decentralized control scheme based on virtual-passivity. It is shown that the cooperative AGV systems ensure stability and the convergence to scaled multiple of each desired velocity field for multiple AGV systems. Finally, the application of proposed virtual passivity-based decentralized control algorithm via system augmentation is applied to be the tracking a circle. Also. the simulation results for the object-transportation by two AGV systems illustrate the validity of the proposed control scheme.

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분산 수동속도장 제어법을 이용한 다중 AGV 시스템의 협조 이송제어 (A Cooperative Object-Transportation Control of Multiple AGV Systems using Decentralized Passive Velocity Field Control Algorithm)

  • 서진호;김영복;이권순
    • 대한전기학회논문지:시스템및제어부문D
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    • 제55권6호
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    • pp.261-263
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    • 2006
  • Automatic guided vehicle(AGV) in the factory has an important role to advance the flexible manufacturing system. In this paper, we propose a novel object-transportation control algorithm of cooperative AGV systems to apply decentralized control scheme based on virtual-passivity. It is shown that the cooperative AGV systems ensure stability and the convergence to scaled multiple of each desired velocity field for multiple AGV systems. Finally, the application of p reposed virtual passivity-based decentralized control algorithm via system augmentation is applied to be the tracking a circle. Also, the simulation results for the object-transportation by two AGV systems illustrate the validity of the proposed control scheme.

화재 재난 상황 인식을 위한 객체 검출 (Object detection for Fire Disaster Situation Recognition)

  • 김태성;방재연;서정운;손경아
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2022년도 추계학술발표대회
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    • pp.426-428
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    • 2022
  • 화재 상황에서의 빠른 현장 파악은 인명피해를 줄이는데 중요한 요소이다. 기존 연구의 화재와 관련된 데이터셋들은 대부분 불과 연기를 라벨링하여 화재의 예방에 초점을 두고 있다. 본 연구에서는 화재 상황에서 사람과 소방관, 연기, 불을 탐지하는 Object detection 모델을 만들어 현장 파악에 더욱 도움을 주고자 하였다. 이를 위해 화재 상황 이미지 약 3000장을 수집하고 라벨링하여 데이터셋을 구성하였으며 이를 이용해 객체 검출 모델인 RetinaNet을 학습하였다. 또한, 화재 상황에서 Object Detection 모델의 성능을 향상시키기 위해 기존 모델인 RetinaNet에 Dehazing(FFA-Net), Smoke augmentation, semi-supervised(ISD) 방법을 적용하였고, semi-supervised 조건에서 mAP 63.7로 가장 높은 성능을 도출하였다.