Fog detection using an artificial neural network

Abstract: This project studies a method of image-based fog detection directly from a camera without using the transmissometer. Fog can be detected using transmissometers which could be a very costly approach. This thesis presents an image-based approach for fog detection using Artificial Neural networks. A neural network model will be used to classify images either into two classes, whether it contains fog or not, or into multiple fog intensity classes, based on visibility derived from Koschmieder's Law. By applying neural network model foggy weather can be detected directly from surveillance cameras and makes fairly accurate predictions, which is useful in many aspects of industry and life. The goal of the thesis is to improve on previous methods that have used neural networks for similar tasks. To achieve the goal, the work starts with creating a suitable dataset for training and validation based on a combination of real images and images generated from Unreal Engine or other simulation scrips. And then, create an effective model based on neural network with the dataset to solve the classification problem. Finally, validate the completed model and implement it on a real surveillance camera to test the actual performance, the model will be retrained if necessary according to the test result.