研究镜头光圈对车载目标检测的影响，提升仿真真实度。
On the Relation between Optical Aperture and Automotive Object Detection
- 用点扩散函数模拟光学效应，还原真实成像
- 仿真图像与真实图像的域差距显著减小
- 适合自动驾驶感知系统研发人员参考
本文研究了光圈大小和形状对基于深度学习的车载摄像头系统在交通标志识别和信号灯状态检测任务中的影响。提出一种基于点扩散函数（PSF）的光学效应模拟方法，增强合成图像的真实感，有效降低合成数据与真实世界图像之间的域差距。通过该技术对计算机生成场景进行优化，建模光学畸变，显著提升了仿真精度，为自动驾驶视觉感知系统的开发提供了更可靠的训练数据。
原文摘要 · Abstract (English)
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.
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