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

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.