• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.3
• /
• pp.300-310
• /
• 2022

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.91-98
• /
• 2023

Human-object interaction(HOI) detection requires both object detection and interaction recognition, and requires a large amount of data to learn a detection model. Current opened dataset is insufficient in scale for training model enough. In this paper, we propose an easy and effective data augmentation method called Simple Quattro Augmentation(SQA) and Random Quattro Augmentation(RQA) for human-object interaction detection. We show that our proposed method can be easily integrated into State-of-the-Art HOI detection models with HICO-DET dataset.

• The Journal of Korea Robotics Society
• /
• v.14 no.1
• /
• pp.14-21
• /
• 2019

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.195-204
• /
• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• Journal of Broadcast Engineering
• /
• v.27 no.1
• /
• pp.13-19
• /
• 2022

Video Object Segmentation (VOS) task requires an amount of labeled sequence data, which limits the performance of the current VOS methods trained with public datasets. In this paper, we propose two effective data augmentation schemes for VOS. The first augmentation method is to swap the background segment to the background from another image, and the other method is to play the sequence in reverse. The two augmentation schemes for VOS enable the current VOS methods to robustly predict the segmentation labels and improve the performance of VOS.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.3
• /
• pp.375-386
• /
• 2024

Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

• The Journal of Korea Robotics Society
• /
• v.15 no.2
• /
• pp.184-189
• /
• 2020

This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1214-1222
• /
• 2020

Pervasive augmented reality (AR) technology can be used to efficiently search for the required information regarding products in stores through text augmentation in an Internet of Things (IoT) environment. The evolution of context awareness and image processing technologies are the main driving forces that realize this type of AR service. One of the problems to be addressed in the service is that augmented objects are fixed and cannot be replaced efficiently in real time. To address this problem, a real-time mobile AR framework is proposed. In this framework, an optimal object to be augmented is selected based on object similarity comparison, and the augmented objects are efficiently managed using distributed metadata servers to adapt to the user requirements, in a given situation. To evaluate the feasibility of the proposed framework, a prototype system was implemented, and a qualitative evaluation based on questionnaires was conducted. The experimental results show that the proposed framework provides a better user experience than existing features in smartphones, and through fast AR service, the users are able to conveniently obtain additional information on products or objects.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.645-647
• /
• 2021

본 논문에서는 목조건물의 Crack만을 움직여 Data set을 증강하는 기법을 제안한다. 이 기법은 이미지 내 Crack Detection의 학습 데이터를 만들기 위해 이미지의 전체적인 값으로 Flip, Rotation, Shift, Rescale 등의 변환을 통해 Data Augmentation을 진행하는 대신 Crack이라는 하나의 Object만을 가지고 새로운 데이터를 생성한다. 이때 Object는 관심 영역 내에서만 연산되어 기존의 방법보다 더욱 많은 데이터를 얻을 수 있으며, Crack이 관심 영역 밖으로 이동하지 않기 때문에 이상치 혹은 결측치가 존재하지 않는 데이터를 얻을 수 있다. 또한 Crack이 존재하지 않는 이미지에도 임의적으로 Crack을 생성하여 새로운 데이터를 만들 수 있다. 결론적으로 본 논문에서는 Crack Detection의 학습을 위하여 기존 방법보다 우수한 성능의 Data Augmentation을 제안하였다.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.5
• /
• pp.289-296
• /
• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.