Evaluating Anomaly Factors in Images
Date of Award
12-12-2024
Degree Name
Ph.D. in Computer Science
Department
Department of Computer Science
Advisor/Chair
Tam Nguyen
Abstract
Evaluating anomaly factors in images is a pivotal element in advancing the robustness of image processing techniques, particularly under adverse and dynamic conditions. This thesis presents a comprehensive investigation into anomaly factors, focusing on two major evaluations: anomaly addition and anomaly removal. In the first evaluation, anomaly addition, we assess the resilience of computer vision frameworks in real-world scenarios. Specifically, this involves studying the performance of object detection algorithms in adverse weather conditions, such as fog, rain, snow, and sun flare, which pose significant challenges to autonomous vehicle technologies. Our methodology includes calculating Intersection over Union (IoU) to measure bounding box overlap between model predictions and ground truth labels, allowing for an accurate assessment of true positives (TP), false positives (FP), and false negatives (FN) across multiple classes. We use performance metrics such as class accuracy, precision, recall, F1 score, and average accuracy to provide a comprehensive view of model robustness. Through ablation studies and dual-modality architecture analysis, the impact of these anomalies on traffic monitoring, vehicle tracking, and object detection is thoroughly examined. The findings underscore the limitations of algorithms trained under clear weather conditions and emphasize the need for more adaptive systems to ensure safety and efficiency in intelligent transportation technologies. The second evaluation, anomaly removal, explores the effectiveness of image inpainting techniques in removing undesired elements, such as photobombing, from images. A benchmarking study was conducted to compare state-of-the-art inpainting methods on a dataset of over 300 images. Using performance metrics like PSNR, SSIM, and FID, the results reveal both the strengths and limitations of current techniques in restoring images with varying levels of complexity. Our evaluation provides a valuable reference for selecting appropriate inpainting methods for practical applications in image restoration. This thesis contributes to the growing field of computer vision by presenting insights into improving anomaly management, which is crucial for the development of robust and reliable image processing systems in diverse real-world conditions.
Keywords
Real-World Object Detection Challenges, Performance Evaluation in AI, Machine Learning Algorithms, Computer Vision Applications, Anomaly Detection in Images, Adverse Weather Image Detection, AI for Real-World Applications, Photobomb Removal Solutions, Image Inpainting Techniques, Adverse Driving Conditions Dataset, De-Photobombing Dataset
Rights Statement
Copyright © 2024, author.
Recommended Citation
Patel, Vatsa Sanjay, "Evaluating Anomaly Factors in Images" (2024). Graduate Theses and Dissertations. 7482.
https://ecommons.udayton.edu/graduate_theses/7482
