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

Publisher

MDPI AG

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