Annotation is essential in machine learning. Building an accurate object detec-tion model requires a large, diverse dataset, which poses challenges due to thetime-consuming nature of manual annotation. This thesis was made in collabora-tion with Project Ngulia, which aims at developing technical solutions to protectand monitor wild animals. A contribution of this work was to integrate an effi-cient semi-automatic image annotation tool within the Ngulia system, with theaim of streamlining the annotation process and improving the employed objectdetection models. Through research into available annotation tools, a custom toolwas deemed the most cost-effective and flexible option. It utilizes object detec-tion model predictions as annotation suggestions, improving the efficiency of theannotation process. The efficiency was evaluated through a user test, with partic-ipants achieving an average reduction of approximately 2 seconds in annotationspeed when utilizing suggestions. This reduction was supported as statisticallysignificant through a one-way ANOVA test. Additionally, it was investigated which images should be prioritized for an-notation in order to obtain the the most accurate predictions. Different samplingmethods were investigated and compared. The performance of the obtained mod-els remained relatively consistent, although with the even distribution methodat top. This indicate that the choice of sampling method may not substantiallyimpact the accuracy of the model, as the performance of the methods was rela-tively comparable. Moreover, different methods of selecting training data in there-training process was compared. The difference in performance was consider-ately small, likely due to the limited and balanced data pool. The experimentsdid however indicate that incorporating previously seen data with unseen datacould be beneficial, and that a reduced dataset can be sufficient. However, furtherinvestigation is required to fully understand the extent of these benefits.