How High can you Detect? Improved accuracy and efficiency at varying altitudes for Aerial Vehicle Detection

Object detection in aerial images is a challenging task mainly because of two factors, the objects of interest being really small, e.g. people or vehicles, making them indistinguishable from the background; and the features of objects being quite different at various altitudes. Especially, when utilizing Unmanned Aerial Vehicles (UAVs) to capture footage, the need for increased altitude to capture a larger field of view is quite high. In this paper, we investigate how to find the best solution for detecting vehicles in various altitudes, while utilizing a single CNN model. The conditions for choosing the best solution are the following; higher accuracy for most of the altitudes and real-time processing ( >20 Frames per second (FPS) ) on an Nvidia Jetson Xavier NX embedded device. We collected footage of moving vehicles from altitudes of 50-500 meters with a 50-meter interval, including a roundabout and rooftop objects as noise for high altitude challenges. Then, a YoloV7 model was trained on each dataset of each altitude along with a dataset including all the images from all the altitudes, and several training and evaluation experiments were conducted. Overall, the best method for achieving optimal trade-off between accuracy and inference speed is to training on a mixed dataset of multiple altitudes and tune the inference speed to the smaller image size. Dataset: https://zenodo.org/record/7736336"© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." R. Makrigiorgis, C. Kyrkou and P. Kolios, "How High can you Detect? Improved accuracy and efficiency at varying altitudes for Aerial Vehicle Detection," 2023 International Conference on Unmanned Aircraft Systems (ICUAS), Warsaw, Poland, 2023, pp. 167-174, doi: 10.1109/ICUAS57906.2023.10156376