Additionally, NST has been used to create a stylized version of the COCO (Common Objects in Context) dataset, mimicking images obtained from thermal imagers [21], and enhancing object detection models for personal protective equipment (PPE) detection by generating extreme conditions such as low light, strong light, dust, fog, and rain [22]. An alternative approach to enhance object detection accuracy under adverse weather conditions is noise removal techniques, also called image restoration. This method involves preprocessing images before object detection to improve image quality and eliminate noise. During this preprocessing phase, a noise removal module is established prior to the object detection module. This module is trained to remove noise generated by adverse weather conditions from the images, restoring them to cleaner versions. This method can generally be classified into three categories [23]. The first category involves establishing a noise removal module using physical and mathematical models or deep learning networks [24; 25; 26; 27; 28], followed by utilizing a pre-trained object detection model for detection tasks. The second and third categories consider the loss of object detection accuracy when establishing the noise removal module, incorporating the detection results into the design of the loss function partially or entirely. In the second category, the parameters of the pre-trained object detection model are learnable [29; 30; 31; 32], while in the third category, they remain fixed [23; 33]. YOLO series of models represent the most widely used approach to date. From YOLOv1 to YOLOv9, this series of models has played a pivotal role in the field of object detection, with various variants based on each version. YOLOv1, introduced by [PERSON] et al. in 2015 [34], was notable for its ability to simultaneously perform object class classification and bounding box prediction, earning recognition for its speed and efficiency. In 2017, YOLOv2 [35] was introduced, further refining YOLOv1 by introducing the Darknet-19 architecture and a series of techniques, resulting in improved model performance, comparable speed, and support for a greater number of object classes. YOLOv3 [36], introduced in 2018, adopted the Darknet-53 backbone architecture, further enhancing model efficiency. YOLOv4 [37], introduced in 2020 by [PERSON] et al., introduced the CSPDarknet53 backbone architecture and multiple new techniques, including Mosaic data augmentation and a new anchor-free detection head, aimed at improving detection accuracy. In 2022, the team presented YOLOv7 [38], which adopted the Extended Effective Layer Aggregation Network (E-ELAN) and RepVGG, enhancing network learning capabilities through shuffling and merging cardinalities without disrupting the original gradient path. Following this, YOLOv9 [39] was unveiled in 2024, introducing groundbreaking technologies such as Programmable Gradient Information (PGI) and the General Efficient Layer Aggregation Network (GELAN), achieving significant improvements in efficiency, accuracy, and adaptability. YOLOv5 [40], proposed by [PERSON], founder and CEO of Ultralytics, in 2020 modified the CSPDarknet53 backbone network, improving upon aspects of YOLOv4. It was developed in PyTorch instead of Darknet and incorporated the Ultralytics algorithm AutoAnchor. In 2023, YOLOv8 [41] was introduced, featuring advanced backbone and neck structures, as well as an anchor-free segmentation head from Ultralytics, along with optimized accuracy and speed balance, making it suitable for various real-time object detection tasks. YOLOv6 [42], proposed by [PERSON] in 2022, combined backbone blocks such as RepVGG or CSPStackRep and utilized enhanced quantization techniques such as post-training quantization and channel-level distillation to achieve faster and more accurate detectors. The Vision Transformer (ViT) is a deep learning model based on the Transformer architecture, first proposed by [PERSON] et al.