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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Types of Collisions - II01:19

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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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Elastic Collisions: Case Study01:15

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Types Of Collisions - I01:04

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When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
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Elastic Collisions: Introduction01:00

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Updated: Jul 12, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
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Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review.

Daniel Vera-Yanez1, António Pereira2,3, Nuno Rodrigues2

  • 1Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.

Journal of Imaging
|October 27, 2023
PubMed
Summary
This summary is machine-generated.

This review examines computer-vision-based flying obstacle detection for midair collision avoidance. Research shows growing interest, driven by drone accessibility and improved computing, but real-world testing remains limited.

Keywords:
computer visionmidair collisionobstacle detectionsystematic review

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Aerospace Engineering

Background:

  • Midair collisions pose a significant risk to aviation safety, particularly with the proliferation of unmanned aerial systems.
  • Effective detection of flying obstacles is crucial for developing robust collision avoidance systems.
  • Computer vision offers a promising sensor modality for real-time obstacle detection.

Purpose of the Study:

  • To systematically review and analyze the existing literature on computer-vision-based flying obstacle detection.
  • To identify trends, challenges, and research gaps in the field of midair collision avoidance for flying vehicles.
  • To understand the factors contributing to the growth of research in this domain.

Main Methods:

  • A systematic literature search was conducted across major scientific databases (Scopus, IEEE, ACM, MDPI, Web of Science) covering publications up to 2022.
  • An initial pool of 647 publications was screened, with 85 selected for in-depth analysis.
  • The review focused on articles related to computer-vision-based flying obstacle detection and midair collision avoidance.

Main Results:

  • A notable increase in publications on flying obstacle detection and tracking using computer vision was observed.
  • Hypothesized drivers for this increase include the availability of commercial drones and advancements in single-board computers and computer vision libraries.
  • The majority of reviewed algorithms were evaluated in simulation environments, with only 26% reporting tests on physical flying vehicles.

Conclusions:

  • Future research should prioritize enhancing the success rate of threat detection algorithms.
  • There is a critical need for more testing of proposed solutions in complex, real-world scenarios using physical platforms.
  • Addressing these gaps will be essential for the practical implementation of effective midair collision avoidance systems.