Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather Image Dataset

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance.