Deep learning-based long-distance optical UAV detection: color versus grayscale

Denis Ojdanić; Christopher Naverschnigg; Andreas Sinn; Georg Schitter

doi:10.1117/12.2663318

13 June 2023 Deep learning-based long-distance optical UAV detection: color versus grayscale

Denis Ojdanić, Christopher Naverschnigg, Andreas Sinn, Georg Schitter

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 125270B (2023) https://doi.org/10.1117/12.2663318
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

This paper presents a comparison between grayscale and color-based deep learning algorithms for long distance optical UAV detection using robotic telescope systems. Three deep learning object detection algorithms are trained with a custom dataset consisting of RGB images and the performance is evaluated against the same algorithms trained with the same dataset converted to grayscale. Network training from scratch and fine-tuning are evaluated. The results for all algorithms show that fine-tuning with RGB images maximizes the detection performance and scores about 5% better in terms of mean average precision (mAP(0.5)) compared to fine-tuning on grayscale images.

Conference Presentation

Citation Download Citation

Denis Ojdanić, Christopher Naverschnigg, Andreas Sinn, and Georg Schitter "Deep learning-based long-distance optical UAV detection: color versus grayscale", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 125270B (13 June 2023); https://doi.org/10.1117/12.2663318

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