Safe Landing Zone Detection for UAVs using Image Segmentation and Super Resolution

Increased usage of UAVs in urban environments has led to the necessity of safe and robust emergency landing zone detection techniques. This paper presents a novel approach for detecting safe landing zones for UAVs using deep learning-based image segmentation. Our approach involves using a custom dataset to train a CNN model. To account for low-resolution input images, our approach incorporates a Super-Resolution model to upscale low-resolution images before feeding them into the segmentation model. The proposed approach achieves robust and accurate detection of safe landing zones, even on low-resolution images. Experimental results demonstrate the effectiveness of our method and show a marked improvement of upto 6.3% in accuracy over state-of-the-art safe landing zone detection methods.

In IAPR Machine Vision Applications 2023, Hamamatsu, Japan