IoT (Internet of Things) is acommunication network that connects physical or things to each other or with a group all together. The use is widely popular nowadays and its usage has expanded into interesting subjects. Especially, it is getting more popular to research in cross subjects such as mixing smart systems with computer sciences and engineering applications together. Object detection is one of these subjects. Realtime object detection is one of the foremost interesting subjects because of its compute costs. Gaps in methodoloy, unknown concepts and insufficiency in mathematical modeling makes it harder for designing these computing algorithms. Algortihms in these applications can be developed with in machine learning and/or numerical methods that are available in scientific literature. These operations are possible only if communication of objects within theirselves in physical space and awareness of the objects nearby. Artificial Neural Networks may help in these studies. In this study, yolo algorithm which is seen as a key element for real-time object detection in IoT is researched. It is realized and shown in results that optimization of computing and analyzation of system aside this research which takes Yolo algorithm as a foundation point . As a result, it is seen that our model approach has an interesting potential and novelty.