Learning to Track Aircraft in Infrared Imagery

Wu, Sijie; Zhang, Kai; Li, Shaoyi; Yan, Jie

doi:10.3390/rs12233995

Cited by 7 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from the passive landing marker methods listed above, the active sourced markers presented in some early studies are potential solutions to nighttime landing. Activated landing markers, a large number of which are based on TIR technology that has been widely used for rescue, surveillance, inspection, and automatic target recognition [ 40 , 41 ], are more favorable to operating in bad weather conditions or dark environments. Several studies have suggested deploying infrared-based ground structures and sensors to accommodate the task of landing in poor-illuminated scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

View full text Add to dashboard Cite

Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The thermal infrared technology has been widely used for rescue, surveillance, and automatic target recognition. Tracking based on this technology is not subject to illumination variations; thus, it can track the target in total darkness [38]. The heat signatures of electrical components captured by the FLIR camera are not affected by the surroundings such as the sky or clouds because the camera is capable of their recognition.…”

Section: Detection Of Structural Damagesmentioning

confidence: 99%

The Use of UAV with Infrared Camera and RFID for Airframe Condition Monitoring

et al. 2021

View full text Add to dashboard Cite

The new progressive smart technologies announced in the fourth industrial revolution in aviation—Aviation 4.0—represent new possibilities and big challenges in aircraft maintenance processes. The main benefit of these technologies is the possibility to monitor, transfer, store, and analyze huge datasets. Based on analysis outputs, there is a possibility to improve current preventive maintenance processes and implement predictive maintenance processes. These solutions lower the downtime, save manpower, and extend the components’ lifetime; thus, the maximum effectivity and safety is achieved. The article deals with the possible implementation of an unmanned aerial vehicle (UAV) with an infrared camera and Radio Frequency Identification (RFID) as two of the smart hangar technologies for airframe condition monitoring. The presented implementations of smart technologies follow up the specific results of a case study focused on trainer aircraft failure monitoring and its impact on maintenance strategy changes. The case study failure indexes show the critical parts of aircraft that are subjected to damage the most. The aim of the article was to justify the need for thorough monitoring of critical parts of the aircraft and then analyze and propose a more effective and the most suitable form of technical condition monitoring of aircraft critical parts. The article describes the whole process of visual inspection performed by an unmanned aerial vehicle (UAV) with an IR camera and its related processes; in addition, it covers the possible usage of RFID tags as a labeling tool supporting the visual inspection. The implementations criteria apply to the repair and overhaul small aircraft maintenance organization, and later, it can also increase operational efficiency. The final suggestions describe the possible usage of proposed solutions, their main benefits, and also the limitations of their implementations in maintenance of trainer aircraft.

show abstract

“…Tracking and segmenting small targets on edge devices is a task of great significance, which has a wide range of applications in air traffic management, military reconnaissance, aviation security, and more [1][2][3][4]. Semi-supervised video object segmentation (S-VOS) is a task that only requires the user to specify the object of interest in the first frame and then automatically completes the tracking and segmentation of the target in the subsequent frames.…”

Section: Introductionmentioning

confidence: 99%

MobileSAM-Track: Lightweight One-Shot Tracking and Segmentation of Small Objects on Edge Devices

Liu,

Zhao,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Tracking and segmenting small targets in remote sensing videos on edge devices carries significant engineering implications. However, many semi-supervised video object segmentation (S-VOS) methods heavily rely on extensive video random-access memory (VRAM) resources, making deployment on edge devices challenging. Our goal is to develop an edge-deployable S-VOS method that can achieve high-precision tracking and segmentation by selecting a bounding box for the target object. First, a tracker is introduced to pinpoint the position of the tracked object in different frames, thereby eliminating the need to save the results of the split as other S-VOS methods do, thus avoiding an increase in VRAM usage. Second, we use two key lightweight components, correlation filters (CFs) and the Mobile Segment Anything Model (MobileSAM), to ensure the inference speed of our model. Third, a mask diffusion module is proposed that improves the accuracy and robustness of segmentation without increasing VRAM usage. We use our self-built dataset containing airplanes and vehicles to evaluate our method. The results show that on the GTX 1080 Ti, our model achieves a J&F score of 66.4% under the condition that the VRAM usage is less than 500 MB, while maintaining a processing speed of 12 frames per second (FPS). The model we propose exhibits good performance in tracking and segmenting small targets on edge devices, providing a solution for fields such as aircraft monitoring and vehicle tracking that require executing S-VOS tasks on edge devices.

show abstract

Learning to Track Aircraft in Infrared Imagery

Cited by 7 publications

References 46 publications

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

The Use of UAV with Infrared Camera and RFID for Airframe Condition Monitoring

MobileSAM-Track: Lightweight One-Shot Tracking and Segmentation of Small Objects on Edge Devices

Contact Info

Product

Resources

About