KDP-Net: An Efficient Semantic Segmentation Network for Emergency Landing of Unmanned Aerial Vehicles

Abstract

As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.