Human Detection using Real-virtual Augmented Dataset

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 ima...

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Bibliographic Details
Published in:Journal of information and communication convergence engineering Vol. 21; no. 1; pp. 98 - 102
Main Authors: Jongmin, Lee, Yongwan, Kim, Jinsung, Choi, Ki-Hong, Kim, Daehwan, Kim
Format: Journal Article
Language:English
Published: 한국정보통신학회JICCE 2023
Subjects:
Data augmentation
YOLO etc
Semi-synthetic data
Human detection
ISSN:2234-8255, 2234-8883
Online Access:Get full text
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Summary: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.
Bibliography:KISTI1.1003/JNL.JAKO202310740930438
ISSN:2234-8255
2234-8883
DOI:10.56977/jicce.2023.21.1.98