YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet
Author:
Dai Wei1ORCID, Zhai Zhengjun1ORCID, Wang Dezhong2, Zu Zhaozi2, Shen Siyuan1ORCID, Lv Xinlei1, Lu Sheng1, Wang Lei1
Affiliation:
1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China 2. AVIC Xi’an Flight Automatic Control Research Institute, Xi’an 710076, China
Abstract
The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft.
Funder
Ministry of Industry and Information Technology of the People’s Republic of China
Reference36 articles.
1. Wang, Z., Zhao, D., and Cao, Y. (2022). Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle. Aerospace, 9. 2. Airport localization based on contextual knowledge complementarity in large scale remote sensing images;Guo;EAI Endorsed Trans. Scalable Inf. Syst.,2022 3. Airport Detection Based on Improved Faster RCNN in Large Scale Remote Sensing Images;Yin;Sens. Imaging,2020 4. Wang, Q., Feng, W., Yao, L., Zhuang, C., Liu, B., and Chen, L. (2023). TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion. Remote Sens., 15. 5. Li, H., Kim, P., Zhao, J., Joo, K., Cai, Z., Liu, Z., and Liu, Y. (2020, January 17). Globally optimal and efficient vanishing point estimation in atlanta world. Proceedings of the European Conference on Computer Vision, Glasgow, UK.
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