On the Relation between Optical Aperture and Automotive Object Detection
This work addresses the domain gap issue for automotive vision systems, but it is incremental as it builds on existing simulation techniques with optical refinements.
The paper tackled the problem of domain gap between synthetic and real-world images in automotive camera systems by simulating optical effects like aperture size and shape using the point spread function (PSF), resulting in enhanced realism and improved simulation accuracy for tasks such as traffic sign recognition and light state detection.
We explore the impact of aperture size and shape on automotive camera systems for deep-learning-based tasks like traffic sign recognition and light state detection. A method is proposed to simulate optical effects using the point spread function (PSF), enhancing realism and reducing the domain gap between synthetic and real-world images. Computer-generated scenes are refined with this technique to model optical distortions and improve simulation accuracy.