Modelling performance of AI/ML-based tower localization in thermal imagery

L. Zhang; M. Martino; A. Irwin; J. Mares; O. Furxhi; C. K. Renshaw

doi:10.1117/12.2663625

14 June 2023 Modelling performance of AI/ML-based tower localization in thermal imagery

L. Zhang, M. Martino, A. Irwin, J. Mares, O. Furxhi, C. K. Renshaw

Proceedings Volume 12533, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV; 1253305 (2023) https://doi.org/10.1117/12.2663625
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Object detection, a critical task in computer vision, has been revolutionized by Deep Learning technologies, especially convolutional neural networks (CNN). These techniques are increasingly deployed in infrared imaging systems for long-range target detection, localization, and identification. Its performance is highly dependent on the training procedure, network architecture and computing resources. In contrast, human-in-the-loop task performance can be reliably predicted using well-established models. Here we model the performance of a CNN developed for MWIR and LWIR sensors and compare against human perception models. We focus on tower detection relevant to vision-based geolocation tasks which present novel high-aspect ratio, unresolved and low-clutter scenarios.

Conference Presentation

Citation Download Citation

L. Zhang, M. Martino, A. Irwin, J. Mares, O. Furxhi, and C. K. Renshaw "Modelling performance of AI/ML-based tower localization in thermal imagery", Proc. SPIE 12533, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV, 1253305 (14 June 2023); https://doi.org/10.1117/12.2663625

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