Nathan Ferrence | 2025 I.S. Symposium
狈补尘别:听Nathan Ferrence
罢颈迟濒别:听Understanding the Role of AI-Based Object Detection Models in Autonomous Vehicles
惭补箩辞谤:听Computer Science
Advisor: Asa’d Asa’d
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.
Posted in Symposium 2025 on May 1, 2025.
