This thesis work takes place at the Emerging Technologies department of Volvo Construction Equipment(CE), in the context of a larger project which involves several students. The focus is a mobile robot built by Volvo for testing some AI features such as Decision Making, Natural Language Processing, Speech Recognition, Object Detection. This thesis will focus on the latter. During last 5 years researchers have built very powerful deep learning object detectors in terms of accuracy and speed. This has been possible thanks to the remarkable development of Convolutional Neural Networks as feature extractors for Image Classification. The purpose of the report is to give a broad view over the state-of-the-art literature of Object Detection, in order to choose the best detector for the robot application Volvo CE is working with, considering that the robot's real-time performance is a priority goal of the project. After comparing the different methods, YOLOv3 seems to be the best choice. Such framework will be implemented in Python and integrated with an object tracking system which returns the 3D position of the objects of interest. The result of the whole system will be evaluated in terms of speed and precision of the resulting detection of the objects. ; Detta arbete utförs hos Emerging Technologies på Volvo Construction Equipment(CE) i ett stort projekt som involverar flera studenter. Arbetes fokus är att använda en robot skapad av Volvo för att testa olika AI tekniker såsom beslutsfattandeg, naturlig språkbehandling, taligenkänning, objektdetektering. Denna uppsats kommer att behandla den sistnämnda tekniken. Under de 5 senaste åren har forskning visat att det är möjligt att bygga kraftfulla deep learning object detectors vad gäller att korrekt identifera samt snabbt detektera objekt. Allt detta är möjligt tack vare ramverket Convolutional Neural Networks som agerar som feature extractors för Image Classification. Målet med denna rapport är att ge en generell överblick över det senaste inom objektdetektering för ...