Full
Outline
In the process of testing intelligent vehicle camera sensor functionality in foggy environments, real-world fog test scenarios face high acquisition costs, poor repeatability, and uncontrollable visibility, while virtual simulation methods lack sufficient realism. To address these issues, this paper proposes a simulation test method specifically for camera sensors in fog environments. First, atmospheric light in different scene regions is estimated via semantic segmentation; next, combining target visibility and fog microphysical parameters, the atmospheric scattering coefficient is computed using a scattering model, and depth information is corrected with the camera intrinsic and extrinsic parameters to produce an accurate transmission map; finally, a real, controllable fog test platform is constructed, and the proposed method is validated through image quality evaluation (PSNR/SSIM) and YOLO-V5-based object recognition tests. Experimental results demonstrate that this method generates high-fidelity foggy images across a 25~100 m visibility range and outperforms existing simulation approaches in both image quality and functional testing, significantly reducing data-collection costs while ensuring repeatable, controllable, and high-quality camera sensor validation.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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