Investigating Object Detection and Semantic Segmentation Using Preprocessed Radar Data

Sammanfattning

While cameras are the most prevalent devices used in physical surveillance and monitoring, there are situations where they are ineffective. In adverse weather conditions, darkness or privacy-sensitive contexts, there are excellent opportunities to replace or complement cameras with radar. There are advanced and successful computer vision solutions for cameras, in areas such as object detection or semantic segmentation. However, the equivalent solutions are potentially underutilized for radar. As with cameras, computer vision applied on radar data could be potentially very useful and have a variety of applications. Of interest to this thesis specifically is the possibility of using computer vision techniques for optimizing radar signal processing. To this end, this thesis aims to investigate the potential of instantaneous object detection and semantic segmentation on preprocessed radar data. A novel annotation framework, which is automated and camera-assisted, is developed to generate a custom data set. Three models are implemented and tested: AdaBoost (classifier), YOLOv8 (state-of-the-art object detection) and an adapted U-Net (semantic segmentation). The results indicate that object detection and semantic segmentation based on single frames of radar data generated early in the signal processing chain is not only feasible, but promising.