Logo des Repositoriums

Auflistung nach Schlagwort "Synthetic Data"

1 - 2 von 2
  • Konferenzbeitrag
    Build Your Own Training Data - Synthetic Data for Object Detection in Aerial Images
    (Software Engineering 2022 Workshops, 2022) Laux, Lea; Schirmer, Sebastian; Schopferer, Simon; Dauer, Johann
    Machine learning has become one of the most widely used techniques in artificial intelligence, especially for image processing. One of the biggest challenges in developing an accurate image processing model is to collect large amounts of data that are suffi ciently close to the real-world scenario. Ideally, real-world data is therefore used for model training. Unfortunately, real-world data is often insuffi ciently available and expensive to generate. Therefore, models are trained using synthetic data. However, there is no standardized method of how training data is generated and which properties determine the data quality. In this paper, we present fi rst steps towards the generation of large amounts of data for human detection based on aerial images. To create labeled aerial images, we are using Unreal Engine and AirSim. We report on fi rst impressions of the generated labeled aerial images and identify future challenges – current simulation tools can be used to create realistic and diverse images including labeling, but native support would be benefi cial to ease their usage.
  • Konferenzbeitrag
    Time flies by: Analyzing the Performance Impact of Ageing in Face Recognition with Synthetic Data
    (BIOSIG 2022, 2022) Marcel Grimmer, Haoyu Zhang
    The vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism. In biometric applications, the main motivation for using synthetic data is to solve the shortage of publicly-available biometric data while reducing privacy risks when processing such sensitive information. These advantages are exploited in this work by simulating human face ageing with recent face age modification algorithms to generate mated samples, thereby studying the impact of ageing on the performance of an open-source biometric recognition system. Further, a real dataset is used to evaluate the effects of short-term ageing, comparing the biometric performance to the synthetic domain. The main findings indicate that short-term ageing in the range of 1-5 years has only minor effects on the general recognition performance. However, the correct verification of mated faces with long-term age differences beyond 20 years poses still a significant challenge and requires further investigation.