## Appendix A
### Specific Introduction of Some Factors  
Gaussian noise refers to statistical noise that follows a Gaussian distribution by obscuring fine details, attenuating edges, and generally degrading image quality. This type of noise can originate from various sources, such as sensor imperfections, transmission interferences, or environmental conditions.  
Speckle noise is a granular interference that inherently appears due to the coherent process of the image acquisition. The manifestation of speckle noise can obscure critical details, hinder the extraction of meaningful features, and, subsequently, impede accurate interpretation and analysis of image data.  
Zoom blur simulates the effect of rapid forward or backward movement in the visual field, akin to the effect achieved through zooming a camera lens during exposure. Unlike uniform blurs, zoom blur affects different regions of an image to varying degrees, complicating tasks such as edge detection, feature extraction, and object recognition.  
Motion blur emerges when either the camera or the subject exhibits movement during the exposure time of a single frame. It is characterized by a smearing along the direction of movement, which can obfuscate details and attenuate high-frequency components essential for various computer vision tasks.  
Brightness refers to the perceived intensity of light emanating from an image. It is often subject to various intrinsic and extrinsic factors, such as lighting conditions, surface properties of imaged objects, and sensor characteristics. Uneven or inappropriate brightnesslevels can obscure details, hinder the extraction of meaningful features, and consequently, impede the accurate interpretation and analysis of image data. In this paper, brightness specifically refers to being too bright.  
Darkness significantly impacts the visibility and discernibility of vital features within an image. In dark scenarios, image sensors capture limited photons, often resulting in visuals that are not only dim but also pervaded by noise, thereby diminishing the quality and usability of the acquired data. The subtleties and nuances of objects become veiled in darkness, making vehicle detection particularly arduous.  
Saturation pertains to the intensity of colors in an image. A highly saturated image exhibits vivid and rich colors, while an image with low saturation appears more muted. Over-saturation and under-saturation can both introduce challenges: the former may lead to loss of detail and color clipping, while the latter may obscure color-based features and distinctions.  
JPEG **compression** can reduce image file sizes by quantizing the transformed coefficients and subsequently encoding them. It introduces artifacts and losses of high-frequency information in the image, such as blocking, ringing, and mosquito noise, which can potentially mask critical details and alter visual features.  
Elastic transform involves applying simulated deformations to an image, such as stretching, shearing, and bending, thereby generating new data instances that maintain the intrinsic features of the original image while introducing variability. However, elastic transform will to some extent alter the geometric features of the vehicle, making vehicle detection difficult.