Object detection for car cabin monitoring

Abstract

Object detection in vehicle cabin environments is becoming increasingly essential to improve vehicle safety, enhance user experience, and facilitate the integration of autonomous driving systems. This thesis addresses the challenges associated with detecting objects in the confined and dynamic space of vehicle interiors. By leveraging deep learning techniques, particularly state-of-the-art algorithms from the YOLO series, this work aims to enhance the accuracy and speed of object detection within the vehicle cabin, thereby supporting the development of more advanced in-cabin monitoring systems and improving overall driving safety.