Object Detection in Diverse Environment

Abstract

This project focuses on the creation of a custom dataset for object detection, employing meticulous annotation techniques and diverse data sources. The dataset is evaluated using various object detection models, including YOLOv8, on both the custom dataset and established benchmarks. Through comparative analysis, the quality, diversity, and generalizability of the custom dataset are assessed. Additionally, the impact of dataset quality on model performance is examined, highlighting areas for improvement. Furthermore, the integration of Generative Adversarial Networks (GANs) to augment dataset diversity is explored. Future work involves refining dataset collection methodologies, exploring novel architectures, and integrating domain adaptation techniques to enhance model robustness and applicability in real-world scenarios.