AI-Based Object Recognition on RGBD Camera Images

Abstract

In this thesis, it was researched how RGBD camera images could be implemented in object recognition algorithms and how they affect the performance of the algorithm. In the beginning, the history of artificial intelligence, and what kind of object recognition algorithms there already exists, are presented. Later on, the RGBD image structure is presented and it is analyzed how they are captured with a ZED camera. In the practical part, the YOLOv3 algorithm was implemented, trained with RGB-D Object Dataset, and then evaluated. With the YOLOv3 algorithm, good learning results were achieved. During the evaluation, it occurred that the implemented network was overfitting, and potential reasons for that happening was analyzed and potential solutions discussed. One of the research questions was how well the YOLOv3 compares to earlier research. For this question, there was no clear answer since the used dataset was not split the same way and, thus, direct comparison to earlier research was not possible. In the end, several suggestions about how the implementation could be improved are represented, and also a couple of future research topics are discussed.