## 2 Related Work  
In light of the research content and thematic inquiry of this study, this section undertakes a comprehensive review, centering on data-driven methodologies, particularly emphasizing the utilization of deep learning-based image synthesis techniques to enhance object detection precision. In 2019, [PERSON] et al. [48] pioneered a methodology integrating deep maps, OpenGL, and a convolutional neural network (CNN) to synthetically generate diverse rain patterns. This approach aimed to systematically evaluate and refine deep neural networks (DNNs) by exposing them to a wide array of simulated rain scenarios. The methodology comprised two fundamental phases: initial training of the neural network on a foundational dataset, followed by a robustness assessment through synthetic rain simulations. Consequently, the original dataset underwent augmentation with synthetic rain patterns, encompassing variations in luminance, rainfall intensity, and the impact of rain droplets on surfaces. During the evaluation phase, parameters governing synthetic rain synthesis were meticulously explored, culminating in dataset expansion during the optimization phase. This involved integrating images depicting identified key synthetic rain variations. Subsequently, the neural network underwent retraining using the augmented dataset, resulting in enhanced performance under diverse environmental conditions. Notably, this approach yielded significant improvements in object detection accuracy, as evidenced by substantial enhancements in average precision (AP) scores on the KITTI dataset, with Faster-RCCN and YOLOv3 achieving notable performance gains of 4.37 and 7.33 percentage points, respectively. In 2020, a novel method was introduced by [49], leveraging a combination of generative adversarial network (GAN) and physics-based rendering (PBR) techniques for synthesizing rainy weather images. Validation using YOLOv2 confirmed substantial improvements in object detection accuracy. Similarly, in 2021, [12] explored the feasibility of generating synthetic weather images using a generative adversarial network (GAN). Subsequent studies investigated the use of CycleGAN to generate synthetic adverse weather data [9], resulting in performance enhancements in object detection models under real adverse conditions. Among various detection models, YOLOv5 emerged as a preferred choice due to its superior performance, achieving high mAP scores under different weather conditions. Furthermore, developments such as the NST-YOLOv5 model [22] demonstrated significant improvements in detecting personal protective equipment (PPE) for construction workers, particularly under extreme weather conditions. In 2024, a study by [50] utilized the CycleGAN model to generate images under rainy and foggy weather conditions for Maritime Surveillance Systems' object detection. The research validated the approach by retraining pre-trained models, including YOLOv5, Faster R-CNN, and DETR, with the results indicating superior performance of YOLOv5 compared to DETR. Additionally, [PERSON] et al. [51], in the same year, employed image augmentation and synthesis techniques to generate adverse weather images, including fog, rain, and snow. Initially, original images were collected and subjected to analytical, GAN, and style-transfer methods to generate adverse weather images. Subsequently, different augmentation strategies were applied to both original and synthetically generated images under adverse weather conditions. Finally, the pre-trained YOLOv5-I model was employed to train object detection models, demonstrating the potential of synthetic weather augmentation, with style transfer networks particularly impactful. Furthermore, exploration of adverse weather denoising methods revealed the delicate balance between noise reduction and detection accuracy, underscoring the inherent complexity of this task.