Image classification algorithm performance based on F[lambda]/d

Jonathan G. Hixson; Brian Teaney; Michael F. Finch; Georges Nehmetallah; Ronald Driggers

doi:10.1117/12.3012780

7 June 2024 Image classification algorithm performance based on Fλ/d

Jonathan G. Hixson, Brian Teaney, Michael F. Finch, Georges Nehmetallah, Ronald Driggers

Proceedings Volume 13045, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXV; 130450H (2024) https://doi.org/10.1117/12.3012780
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This paper will take an initial look at the effect of variations in a sensor’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’s performance using a sensors FLD metric value.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jonathan G. Hixson, Brian Teaney, Michael F. Finch, Georges Nehmetallah, and Ronald Driggers "Image classification algorithm performance based on Fλ/d", Proc. SPIE 13045, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXV, 130450H (7 June 2024); https://doi.org/10.1117/12.3012780

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