Object detection in images is a field of image analysis that is searched intensively during the past few years. In this diploma thesis we present a complete object detection method which was created by Viola and Jones in 2001. Haar-like features are used to describe images, while the classification of the candidate regions of an image is performed by a cascade of classifiers created by the AdaBoost algorithm in order to increase detection speed. By using this method, we trained several detectors for interior parts of a car, as well as its exterior, with sample images from the LabelMe dataset. We show and explain the choices that were made in every detector training. The results of the evaluation of every detector are presented in precision-recall and receiver operator characteristic (ROC) diagrams. We also present some conclusions in order to achieve the best results from this method. In this diploma thesis we created a program for semi-automatic annotation of images, which detects objects in images using the presented method.