Abstract
Autonomous navigation is a critical component of autonomous vehicles. This requires precise object detection and real-time decisions. This project explores four state-of-the art object detection algorithms: Faster-RCNN, YOLO, RetinaNet, and SSD. Models were trained on their respective datasets. Implemented using TensorFlow, PyTorch, and OpenCV, assessing their mean average precision score. The results offer insights into optimizing object detection by applying structural changes and data augmentation techniques. This study contributes to the development of efficient AI-driven perception systems for self driving applications.
Advisor
As'ad, Asa'd
Department
Computer Science
Recommended Citation
Ferrence, Nathan, "Understanding the Role of AI-Based Object Detection Models in Autonomous Vehicles" (2025). Senior Independent Study Theses. Paper 11352.
https://openworks.wooster.edu/independentstudy/11352
Publication Date
2025
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
© Copyright 2025 Nathan Ferrence