With the increasing demand for labeled data in machine learning for visual perception tasks, the interest in using synthetically generated data has grown. Due to the existence of a domain gap between synthetic and real data, strategies in domain adaptation are necessary to achieve high performance with models trained on synthetic or mixed data. With a dataset of synthetically blocked fish-eye lenses in traffic environments, we explore different strategies to train a neural network. The neural network is a binary classifier for full blockage detection. The different strategies tested are data mixing, fine-tuning, domain adversarial training, and adversarial discriminative domain adaptation. Different ratios between synthetically generated data and real data are also tested. Our experiments showed that fine-tuning had slightly superior results in this test environment. To fully take advantage of the domain adversarial training, training until domain indiscriminate features are learned is necessary and helps the model attain higher performance than using random data mixing.