# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
###### Abstract
Deep learning has achieved remarkable success in computer vision (CV), natural language processing, and video/speech recognition [25]. Numerous studies have applied these methods to fog image estimation. [PERSON] et al. [7] predicted visibility in various weather conditions by training a random forest model [8] using dark channel, local contrast, and saturation features. [PERSON] and [PERSON] [11] introduced an autoregressive recurrent neural network that leverages the temporal dynamics of atmospheric conditions to predict visibility. [PERSON] et al. [9] enhanced visibility detection accuracy through transfer learning, where pre-trained models improve prediction accuracy without requiring large amounts of training data. [PERSON] et al. [12] proposed a meteorological visibility estimation method based on feature fusion and transfer learning, integrating multiple data sources for more accurate estimations. [PERSON] et al. [13] experimentally evaluated a transfer learning method using particle swarm optimization (PSO) for meteorological visibility estimation. [PERSON] et al. [14] introduced an end-to-end visibility estimation network (FGS-Net) based on statistical feature streams, which demonstrated high effectiveness in fog-prone areas. [PERSON] et al. [15] developed an automatic sea fog detection and visibility estimation method using CCTV images, achieving accurate sea fog detection and visibility distance estimation. [PERSON] et al. [16] proposed a deep learning method for visibility estimation in traffic images, where deep quantification techniques improved visibility prediction accuracy. However, these methods typically rely on training with large, balanced datasets. While deep learning models such as VGG16 and ResNet50 perform well on large-scale datasets, their accuracy significantly declines when sufficient data are not available. In this study, we compare the performance of VGG16, ResNet50, DenseNet169, and our improved Random Forest (RF) model. VGG16/19 [26] employs multiple convolutional layers and max-pooling for feature extraction, though their depth results in high computational costs. ResNet50 [27] addresses the vanishing gradient problem using residual modules, enabling deeper networks with better performance on complex tasks. DenseNet169 [28] improves efficiency through dense connections, while Random Forest [29] enhances model stability by reducing overfitting in decision trees. Furthermore, the dataset we collected often lacks extreme data, resulting in a low proportion of extreme categories. This uneven distribution creates an imbalanced dataset for training purposes, presenting additional challenges in model performance. When the dataset is unevenly distributed, as observed with the QVEData used in [16] and the private dataset in [30], the methods suffer from poor generalization ability. The scarcity of image samples under conditions of dense fog and very dense fog limits the diversity available during training, leading to reduced estimation accuracy. Moreover, algorithm convergence remains a significant challenge. Many traditional methods require extended training times to achieve convergence, especially when handling complex foggy images [31; 32]. In cases of imbalanced or insufficient data, overfitting becomes a common issue, resulting in poor algorithm convergence and reduced method efficiency. To improve the performance of the RF model in estimating fog on insufficient and imbalanced data, we propose a GAN-based data augmentation technique to increase the proportion of low-representation grades in the dataset. This approach reduces the reliance on naturally imbalanced datasets, where high fog density grades are underrepresented. By applying StyleGAN2-ADA [33] for dataset augmentation, the issue of imbalanced data distribution is mitigated. The generated virtual images increase and balance the dataset across different fog density grades, addressing training challenges posed by limited and imperfect data.