Contributions to machine learning in automotive camera and radar perception systems : applications of the sampling method in image classification and deep learning for radar-based object detection / von M. Sc. Weimeng Zhu. Wuppertal, 2023
Inhalt
- Abstract
- Acknowledgement
- Contents
- 1 Introduction
- 2 Fundamentals and Related Works
- 2.1 Fundamentals of Machine Learning
- 2.2 Fundamentals of Computer Vision
- 2.3 Fundamentals of Automotive Radar
- 3 A New Sampling Method for Image Classification with Neural Networks
- 3.1 Traffic Sign Recognition and Computer Vision Challenges
- 3.2 A New Sampling Method in a Convolutional Neural Network: Dense Spatial Translation Network
- 3.2.1 State of the Art
- 3.2.2 Problems and Challenges
- 3.2.3 DSTN Structure
- 3.2.4 DSTN Functionality
- 3.2.5 Training Strategies
- 3.2.6 Analysis of computational complexity
- 3.3 Experiment Results
- 3.3.1 Street View House Numbers Database
- 3.3.2 German Traffic Sign Recognition Benchmark Database
- 3.3.3 Private Traffic Sign Recognition Database
- 3.4 Summary
- 4 Radar Data Recording, Annotation, Description and Sensor Alignment
- 5 A Deep Neural Network for Automotive Radar Perception
- 5.1 State of the Art
- 5.2 Radar Signal Embedding with Deep Neural Network
- 5.3 Sensor Fusion by Deep Neural Network
- 5.4 A New Sampling-Based Recurrent Neural Network Module
- 5.4.1 Recurrent Neural Network
- 5.4.2 Sampling in Neural Network
- 5.4.3 Gated Recurrent Sampler
- 5.4.4 Behavior Study
- 5.5 A Deep Neural Network Radar Perception System
- 6 Conclusion
- A Appendix
- Bibliography
- List of Figures
- List of Tables
- Glossary
- List of Glossaries