Detection of obstacles in the flight path of an aircraft

Gandhi, T.; Yang, Mau-Tsuen; Kasturi, Rangachar; Camps, Octavia; Coraor, L.D.; McCandless, Jeffrey W.

doi:10.1109/taes.2003.1188902

Cited by 58 publications

(11 citation statements)

References 16 publications

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“…Over the past three decades, a two‐stage processing paradigm has emerged for the simultaneous detection and tracking of dim, subpixel‐sized targets (Arnold, Shaw, & Pasternack, 1993; Barniv, 1985; Gandhi, Yang, Kasturi, Camps, Coraor, et al, 2003, 2006). These two stages are (1) an image preprocessing stage that, within each frame, highlights potential targets with attributes of interest and (2) a subsequent temporal filtering stage that exploits target dynamics across frames.…”

Section: Introductionmentioning

confidence: 99%

“…Specific implementations of the morphological filtering approach include the hit‐or‐miss filter (Schaefer & Casasent, 1995), close‐minus‐open filter (Casasent & Ye, 1997), and top‐hat filter (Braga‐Neto, Choudhary, & Goutsias, 2004). Although a large proportion of research has focused on IR images, there are recent examples of morphological filters being incorporated into target detection algorithms operating on visual spectrum images (Carnie, Walker, & Corke, 2006; Gandhi et al, 2003, 2006). Moreover, a sign of the increasing popularity of morphological filters for small target detection is evident in the host of studies undertaken into the issue of parameter design (Yu, Wu, Wu, & Li, 2003; Zeng, Li, & Peng, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Airborne vision‐based collision‐detection system

Lai

Mejias

Ford

2011

Journal of Field Robotics

106

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Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real-time, vision-based collision-detection system suitable for fixed-wing aerial robotics. Using two fixedwing unmanned aerial vehicles (UAVs) to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5-s response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms. C

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Airborne vision‐based collision‐detection system

Lai

Mejias

Ford

2011

Journal of Field Robotics

106

View full text Add to dashboard Cite

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“…Furthermore, it is well known that morphology techniques are computationally intensive requiring parallelised implementation or dedicated hardware to meet real-time requirements. Finally, morphology approaches are noted as returning a large number of false positives [15,16,17] with computationally intensive techniques used to post-process the detections.…”

Section: Algorithm Backgroundmentioning

confidence: 99%

Real-Time Optical Aircraft Detection

Revell

Buchanan

Dixon

2012

Measurement and Control

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The routine use of Uninhabited Air Systems (UAS) in non-segregated airspace presents many technological and regulatory challenges to the aerospace industry.This paper gives an overview of the collision avoidance problem facing UAS designers and provides a discussion of the current optical aircraft detection and tracking approaches. In addition, the paper describes several important challenges for a computer vision based aircraft detection approach that is designed to form part of an optical UAS collision avoidance system. Some initial flight trial results are presented from our prototype and conclusions drawn.

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“…Aerial surveillance systems (ASS) contained with wire detector must be able to extract these small obstacles efficiently to ensure the safety of the aircrafts [2][3][4][5]. However, the problem of wire collision is still a major issue recently.…”

Section: Introductionmentioning

confidence: 99%

Obstacle detection algorithms for aviation

2011

2011 IEEE International Conference on Computer Science and Automation Engineering

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Wires can be hard to see for pilots and thus present a serious hazard to low-flying aerial vehicles. An airborne vision system having ability to detect wires in time to avoid accident collisions is needed. However, for an automatic vision-based wire detector, extracting wires from a cluttered background is the most important and challenging tasks. In this paper, we propose a novel wire detection algorithm based on real outdoor images for aviation. This detection process combines the morphological properties of the wire and the common line segment detector in image processing. We first generate a line pool using a fast line segment detector to mark all the lines in image. Then wires detector is applied according to our designed properties of wires. The experimental results demonstrate that the approach is effective for automatic wire detection.

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Detection of obstacles in the flight path of an aircraft

Cited by 58 publications

References 16 publications

Airborne vision‐based collision‐detection system

Airborne vision‐based collision‐detection system

Real-Time Optical Aircraft Detection

Obstacle detection algorithms for aviation

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