Challenges and Opportunities of Computer Vision Applications in Aircraft Landing Gear

Au, Joy; Reid, David M.; Bill, Andrew

doi:10.1109/aero53065.2022.9843684

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…As mentioned in the references, a precise runway detection method based on YOLOv5 is proposed in [26], a benchmark for airport detection using Sentinel-1 SAR (Synthetic Aperture Radar) is introduced in [66], and several detection approaches based on deep learning principles are presented in [14,[67][68][69]. Presently, only private aviation companies are endeavoring to research deep learning solutions of runway detection by forward-facing cameras located on the aircraft's nose or wings, which has achieved notable success in vision-based autonomous landing systems such as the capabilities of the autonomous taxiing, taking off, and landing of Airbus [70] and the Daedalean project [71]. These advanced methods demonstrate enhanced adaptability to runway features across varying observing distances and significant achievements to address challenges such as differences in aspect ratios, lighting conditions, and noise at the same time.…”

Section: Vision-based Runway Segmentationmentioning

confidence: 99%

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

Wang,

Feng,

Zhao

et al. 2024

Remote Sensing

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Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive “band” information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model’s ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available.

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Section: Vision-based Runway Segmentationmentioning

confidence: 99%

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

Wang,

Feng,

Zhao

et al. 2024

Remote Sensing

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“…DO-160G covers avionics requirements in terms of environmental test conditions and procedures (Sweeney, 2015). For LG, these include waterproofness, shocks and vibrations, brake temperature, atmospheric conditions, lightning, electromagnetic emissions and susceptibility, and contaminants, such as dust and sand (Au et al, 2022).…”

Section: Load Profile Uncertainties and Risk Managementmentioning

confidence: 99%

“…A LG's components must be all tested against a "qualification test plan" to prove its usability in the harshest of environmental conditions (Au et al, 2022). This does not, however, include the component's entire life's combinations, resulting with the need to add experience from the industry and a "system development process" to add to the system's decisions in terms of verification for its use-case on-site.…”

Section: Load Profile Uncertainties and Risk Managementmentioning

confidence: 99%

Certification of machine learning algorithms for safe-life assessment of landing gear

Mir

Perinpanayagam

2022

Front. Astron. Space Sci.

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This paper provides information on current certification of landing gear available for use in the aerospace industry. Moving forward, machine learning is part of structural health monitoring, which is being used by the aircraft industry. The non-deterministic nature of deep learning algorithms is regarded as a hurdle for certification and verification for use in the highly-regulated aerospace industry. This paper brings forth its regulation requirements and the emergence of standardisation efforts. To be able to validate machine learning for safety critical applications such as landing gear, the safe-life fatigue assessment needs to be certified such that the remaining useful life may be accurately predicted and trusted. A coverage of future certification for the usage of machine learning in safety-critical aerospace systems is provided, taking into consideration both the risk management and explainability for different end user categories involved in the certification process. Additionally, provisional use case scenarios are demonstrated, in which risk assessments and uncertainties are incorporated for the implementation of a proposed certification approach targeting offline machine learning models and their explainable usage for predicting the remaining useful life of landing gear systems based on the safe-life method.

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“…According to [26], while object detection models have greatly improved through innovations such as 3D models, robust feature detection and real-time image matching [27], one of the biggest challenges remained how to detect objects of interest in clustered scenes [28], detection of objects with dynamic behavior [29], detection of actionable objects [27], real-time object detection [29] and Occlusion [30].…”

mentioning

confidence: 99%

Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques

CHIDI,

UDANOR,

ANOLIEFO

2024

Preprint

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Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.

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Challenges and Opportunities of Computer Vision Applications in Aircraft Landing Gear

Cited by 3 publications

References 7 publications

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

Certification of machine learning algorithms for safe-life assessment of landing gear

Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques

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