A Relative Attitude Detection Method for Unmanned Aerial Vehicles Based on You Only Look Once Framework - Jingting Qiu; Feifan Yu; Fengrui Xu; Xinmin Chen; Jiqiang Wang (Aerospace)
Variable environments and constrained edge devices pose the significantly challenging task of directly recognizing the relative attitude of unmanned aerial vehicles (UAVs). Furthermore, datasets of UAV landing markers that are accessible to the general public are scarce. To tackle these challenges, we first constructed a dataset on UAV landing markers called the UAV landing marker dataset (ULMD). Then, we enhanced the You Only Look Once (YOLO) model to devise a model specifically tailored for directly recognizing the relative attitude of UAVs, termed UAV relative attitude YOLO (URA-YOLO). Within URA-YOLO, we propose an enhanced multiscale feature fusion (EMF) module that increases the network’s perceptual range and extracts feature information corresponding to various image sizes. Additionally, we propose a lightweight and efficient feature extraction (LE) module to acquire high-dimensional semantic information. Finally, to mitigate background noise interference, we propose an efficient layer aggregation network with convolutional block attention (ELAN-CA) module. The experimental results demonstrate that our model outperforms the baseline by 10.8% in terms of accuracy while occupying a mere 5.8 M in size, representing a reduction of 6.5%, achieving a satisfactory balance between performance and resource consumption.