dSPACE at the Auto.AI Europe 2022
September 28-30, 2022, Berlin, Germany
Auto.AI Europe is the leading technical event on deep learning for SAE Level 4 & 5 automated vehicles that brings together more than 300 top-industry machine learning, neural networks, and perception experts and decision-makers.
Join us to discuss concepts for self-supervised and behavior-based learning, scalable approaches to machine and reinforcement learning, and benchmarking of perceptual and computer vision systems for ADs with your peers in the automotive AI community.
dSPACE will be presenting its hardware and software, and visitors will also have an opportunity to speak to dSPACE technical sales staff about current product innovations.
More information is available on https://www.auto-ai.eu/
Presentation of our colleague
Date: September 29, 11:30 a.m. CET
Title:
Leveraging Simulation Tools for Rare and Unseen Classes in Data Driven Development - Training Deep Learning Object Detectors with Synthetic Data
Abstract:
Collecting labeled real-world data is a time consuming and expensive task in data-driven development. What makes this task even more difficult is that without a data selection strategy, the collected data will result in an unbalanced data set. Even worse, some classes did not even exist at the time of data collection (e. g. the emergence of e-scooters and cargo bikes in recent years). In our talk, we will show how simulation tools can be used to address these issues by generating synthetic training data tailored to your needs.
Key Takeaways:
- Training on mixed data sets of real-world and synthetic data reduces the need for labeled real-world data by up to 70%,
- Enriching the synthetic dataset with instances of an underrepresented class increases object detection performance for this class,
- Synthetic data in the training data set does not affect the performance for dominant classes,
- The dSPACE toolchain supports the entire data-driven development process: data collection, data selection, and data enrichment with synthetic data.
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