Cable Slack Detection for Arresting Gear Application using Machine
  Vision
Ari Goodman Glenn Shevach Sean Zabriskie Dr. Chris Thajudeen
The cable-based arrestment systems are integral to the launch and recovery of
aircraft onboard carriers and on expeditionary land-based installations. These
modern arrestment systems rely on various mechanisms to absorb energy from an
aircraft during an arrestment cycle to bring the aircraft to a full stop. One
of the primary components of this system is the cable interface to the engine.
The formation of slack in the cable at this interface can result in reduced
efficiency and drives maintenance efforts to remove the slack prior to
continued operations. In this paper, a machine vision based slack detection
system is presented. A situational awareness camera is utilized to collect
video data of the cable interface region, machine vision algorithms are applied
to reduce noise, remove background clutter, focus on regions of interest, and
detect changes in the image representative of slack formations. Some algorithms
employed in this system include bilateral image filters, least squares
polynomial fit, Canny Edge Detection, K-Means clustering, Gaussian
Mixture-based Background/Foreground Segmentation for background subtraction,
Hough Circle Transforms, and Hough line Transforms. The resulting detections
are filtered and highlighted to create an indication to the shipboard operator
of the presence of slack and a need for a maintenance action. A user interface
was designed to provide operators with an easy method to redefine regions of
interest and adjust the methods to specific locations. The algorithms were
validated on shipboard footage and were able to accurately identify slack with
minimal false positives.