On the Relation between Optical Aperture and Automotive Object Detection
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916568382832640 |
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| author | Bar-Shalom, Ofer Philipp, Tzvi Kishon, Eran |
| author_facet | Bar-Shalom, Ofer Philipp, Tzvi Kishon, Eran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Relation between Optical Aperture and Automotive Object Detection Bar-Shalom, Ofer Philipp, Tzvi Kishon, Eran Computer Vision and Pattern Recognition 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. |
| title | On the Relation between Optical Aperture and Automotive Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.09456 |