@inproceedings{4ef0c58ce9654b70bd77d52e723b8673,
title = "Image classification algorithm performance based on Fλ/d",
abstract = "This paper will take an initial look at the effect of variations in a sensor{\textquoteright}s Fλ/d metric value (FLD) on the performance of Yolo_v3 (You Only Look Once) algorithm for object classification. The Yolo_v3 algorithm will initially be trained using static imagery provided in the commonly available Advanced Driver Assist System (ADAS) dataset. Image processing techniques will then be used to degrade image quality of the test data set, simulating detector-limited to optics-limited performance of the imagery. The degraded test set will then be used to evaluate the performance of Yolo_v3 for object classification. Results of Yolo_v3 will be presented for the varying levels of image degradation. An initial summary of the results will be discussed along with recommendations for evaluating an algorithm{\textquoteright}s performance using a sensors FLD metric value.",
keywords = "ADAS, computer vision, convolutional neural network, deep learning, FLD, image identification and classification, infrared simulation, NV IPM, YOLO",
author = "Hixson, {Jonathan G.} and Brian Teaney and Finch, {Michael F.} and George Nehmetallah and Ronald Driggers",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXV 2024 ; Conference date: 23-04-2024 Through 25-04-2024",
year = "2024",
doi = "10.1117/12.3012780",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Haefner, {David P.} and Holst, {Gerald C.}",
booktitle = "Infrared Imaging Systems",
}