## 2 Literature Review
### Pre-Processing Tasks  
Many traditional studies focused on image pre-processing techniques, such as RGB color conversion integrating mean filtering, median filtering, and Gaussian filtering to reduce noise and improve the visibility of features. RGB can be used to select a specific area in the original image for adjustment and then replace the original image, like a patching method, which is less like naturally adjusting the entire image. These methods were used to smooth images, reduce noise, and improve the visual quality of images in vehicle management for classification and number plate cases [22; 23; 24], but the adjusted image is either focused on a single object or has many objects without being affected by multiple resolutions. The previous image-enhancement processing can help increase the diversity and scale of training data, thereby preventing overfitting. Histogram equalization (HE) is a representative enhancement technique in both academic and practical applications, and many improved methods of HE have been developed. Moreover, feature extraction in image processing for vehicle cases can be accomplished using different algorithms, such as SIFT and SURF. Extracting relevant corners or edges can simplify the vehicle or object data and make the model more efficient and accurate. Local binary patterns (LBP) are often used to capture important information that is sensitive to lighting and rotation conditions. HOG pays more attention to the information in the image, so it is used to detect objects inside the image [9; 25; 26; 27; 28]. Improve training accuracy by generating powerful feature-extraction methods for processing complex images. Past image pre-processing research has covered many aspects, from basic filter applications to feature extraction. Image-enhancement methods generate data for machine-learning models. Data augmentation can reduce the need for large image datasets and decrease reliance on model training. If the video quality is poor, it will be difficult for the algorithm to find information and provide better performance [29]. Pre-processing is the most important for supervised machine-learning algorithms. The amount of training data grows exponentially with the input space dimensions. The traditional histogram equalization (HE) used the cumulative distribution function (CDF) to convert a color image into a grayscale image and was widely applied to many previous studies [30; 31; 32; 33]. A histogram can denote that the brightness distribution of pixels in an image can illustrate the brightness and contrast. When the peaks of the histogram skew towards the left, it indicates a darker image, whereas when the peaks are concentrated towards the right at 255, it indicates a brighter image. Histogram equalization adjusts the range of pixel brightness distribution and allows the originally concentrated pixelbrightness values in a certain area to be distributed more evenly across the entire range, therefore improving the visual appearance of the image. However, if all data are processed uniformly, it may increase the contrast of background noise. In contrast-enhancement research, the previous techniques used logarithmic, piecewise linear processing gray-scale conversion, and histogram equalization based on uniformly distributed gray-level histograms, bi-histogram equalization (BHE), and multi-scale adaptive histogram equalization [34; 35]. With the expansion of computer vision applications, researchers have begun to focus on how to improve the contrast of images to enhance the details in the images. Color correction plays an important role in imaging, so researchers are beginning to explore how to perform automatic color correction to resolve color issues caused by lighting changes or sensor differences. By using image enhancement, the contrast, brightness, and color balance of the image can be adjusted to improve the visual quality of the image, thereby improving image quality and making it more suitable for model training.