); [EMAIL_ADDRESS] (M.-H.C.)  
[PERSON]  
3 Department of Land Economics, National Chengchi University, Taipei 11605, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
1 GIS Research Center, Feng Chia University, Taichung 40724, Taiwan; [EMAIL_ADDRESS] (C.-Y.H. ); [EMAIL_ADDRESS] (T.-H.L. ); [EMAIL_ADDRESS] (T.-Y.C. ); [EMAIL_ADDRESS] (M.-H.C.)  
[PERSON]  
2 Ph.D. Program for Infrastructure Planning and Engineering, Feng Chia University, Taichung 40724, Taiwan; 75 [EMAIL_ADDRESS]  
[PERSON]  
2 Ph.D. Program for Infrastructure Planning and Engineering, Feng Chia University, Taichung 40724, Taiwan; 75 [EMAIL_ADDRESS]  
[PERSON]  
1 GIS Research Center, Feng Chia University, Taichung 40724, Taiwan; how@gis. diverse needs of practical application scenarios, ultimately bringing greater convenience and security to people's lives and work. Improving object detection performance under adverse weather conditions is crucial for automated visual systems, making it a focal point in computer vision research. Approaches to handling the influence of adverse weather conditions on object detection tasks can generally be categorized into two groups: data-driven methods and image restoration methods. Data-driven approaches aim to train object detection models specifically tailored to varying weather conditions using images affected by adverse weather. However, a significant challenge with such methods lies in acquiring a large and diverse dataset of images captured under adverse weather conditions. Traditional methods involve collecting real images, which is expensive, time-consuming, and often leads to insufficient generalization of trained models, thereby limiting their applicability. Image augmentation and synthesis techniques have emerged as pivotal advancements for data-driven approaches, particularly with the advent of deep learning. These techniques facilitate effective extraction of additional information from existing data, enabling the generation of new images or modifications to existing ones. Consequently, they enhance the robustness of object detection models to adverse weather conditions. Deep learning-based image generation methods, due to their ability to extract deep visual information from images for synthesis, produce images that better meet user needs. Therefore, these methods have become some of the primary approaches for simulating images under adverse weather conditions. Among them, the generative adversarial network (GAN) and its variants are widely employed methods. Of these methods, CycleGAN, proposed by [PERSON] et al. [7], effectively addresses the challenge of precise matching training samples in pix2 Pix and enables training using unpaired samples. CycleGAN extends the potential applications of GAN in various domains, such as autonomous driving research, by mitigating data scarcity issues under adverse weather conditions [8; 9; 10; 11; 12], as well as in closed-circuit television (CCTV) surveillance systems [13], construction site image dataset creation with diverse weather conditions [14], and synthetic sample generation for model training to address the scarcity of specific samples, such as flood scenarios [15]. Moreover, CycleGAN enhances limited day-night image data for creating paired datasets in industrial inspection applications [16] and in medical applications [17]. Neural style transfer (NST) is another notable method. The pioneering work by [PERSON] et al. [18] showcased the remarkable capabilities of the convolutional neural network (CNN) in artistic image creation by separating and recombining image content and style. NST has since become a popular topic in academic literature and industrial applications [19]. Studies have explored the effects of style transfer-based data transformation on CNN classifier accuracy, particularly in automobile detection under adverse winter weather conditions [20].