Object Detection in Adverse Weather Conditions using Machine Learning

Object detection in challenging weather conditions is a formidable hurdle in the realm of computer vision. Unfavorable weather circumstances, encompassing rain, snow, fog, and low light conditions, substantially impede the effectiveness of conventional computer vision algorithms. This research paper introduces an innovative approach to image-to-image translation by harnessing the power of CycleGAN, a deep learning architecture renowned for its proficiency in transmuting images from one domain to another. This study is dedicated to tailoring the CycleGAN framework to the specific task of enhancing images captured in adverse weather conditions, such as rain, fog, and snow. The proposed methodology capitalizes on the deployment of two distinctive generators, trained cyclically, for the transformation of images between a source domain (representing adverse weather) and a target domain (signifying clear weather). These generators are bolstered by discriminator networks, enabling adversarial training to enhance the quality of image translation.