Synthetic Image Generation Model for Intelligent Vehicle Camera Function Testing in Rain and Fog

To ensure that the camera functions (CFs) of intelligent vehicles can operate normally in rain and fog, sufficient tests for CFs are indispensable. In this work, the synthetic image generation model (SIGM) is proposed to generate images to synthesize rain and fog effects for testing CFs. First, to synthesize the rain blur effects on raw images, the geometric-physical fusion model is proposed based on the properties of raindrop microparticles and vehicle features. In this model, the position distribution of raindrops is determined by the geometric model part, and the image pixel effects are realized by the physical model part. Second, the atmospheric scattering model is used to synthesize the fog effect and contains two parts: an airlight estimation model to estimate airlight values and a transmittance estimation model, where the corrected image depth and fog microphysical information based on the Mie theory are used to determine the fog transmittance. Finally, SIGM is validated through real rain and fog in image quality, testing CFs with synthetic images and videos. The result absolute error sum obtained with SIGM (0.45) is smaller than that of the simulation software (0.57) and another synthetic method (0.65), and all unrecognized targets identified by SIGM match the real test results, whereas both the simulation software and another synthetic method fail to accurately test CF. The experimental results indicate that the proposed SIGM has higher authenticity and reliability than testing using the simulation platform and has higher efficiency, controllability, repeatability, and safety than testing using real vehicle.