Visual Object Detector for Vehicle Teleoperation Applications

Sammanfattning: Self-driving vehicles have recently gained attention from vehicle manufacturers due breakthrough in machine learning and AI algorithms. One of the areas that has sparked interest is the improved perception of vehicles by employing accurate real-time object detectors aided by the fast computing resources available. As more vehicles become autonomous, there will be a need to monitor and remotely control vehicles to handle edge case scenarios that are difficult to automate or foresee. This would require streaming of video from the vehicle to a teleoperator driver. Due to network degradation caused by bandwidth fluctuations and handover operations, streaming of the video is not enough. One can improve the experience of teleoperators by highlighting detected objects in the visual scene such as vehicles and pedestrians. The main contribution of this thesis work is a realtime visual object detector that has comparable accuracy to Faster R-CNN. Furthermore, the proposed detector is modular meaning that retraining of the entire model is not required to detect new types of object classes. Finally, the detector is tested on a video with network degradation artifacts to assess it’s performance.