Self-driving vehicles has become a hot topic in today's industry during the past years and companies all around the globe are attempting to solve the complex task of developing vehicles that can safely navigate roads and traffic without the assistance of a driver.  As deep learning and computer vision becomes more streamlined and with the possibility of using fisheye cameras as a cheap alternative to external sensors some companies have begun researching the possibility for assisted driving on vehicles such as electrical scooters to prevent injuries and accidents by detecting dangerous situations as well as promoting a sustainable infrastructure. However training such a model requires gathering large amounts of data which needs to be labeled by a human annotator. This process is expensive, time consuming, and requires extensive quality checking which can be difficult for companies to afford. This thesis presents an application that allows for semi-automatically labeling a dataset with the help of a human annotator and an object detector. The application trains an object detector together with an active learning framework on a small part of labeled data sampled from the woodscape dataset of fisheye distorted images and uses the knowledge of the trained model as well as using a human annotator as assistance to label more data. This thesis examines the labeled data produced by using the application described in this thesis and compares them with the quality of the annotations in the woodscape dataset. Results show that the model can't make any quality annotations compared to the woodscape dataset and resulted in the human annotator having to label all of the data, and the model achieved an accuracy of 0.00099 mAP.