Abstract
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.
Abstract (translated)
基于电缆的抓取系统是板上和岸上飞机和探险陆地设施发射和回收的重要组成部分。这些现代抓取系统依赖于各种机制在抓取周期中吸收来自飞机的能源，将飞机停下来。该系统的一个主要组成部分是电缆与发动机的接口。该接口处的电缆松弛可能会导致效率降低，并且在继续操作前需要进行维护。在本文中，我们提出了一个基于机器视觉的抓取检测系统。 该系统采用了一个情境意识相机来收集电缆接口区域的视频数据，并应用了机器视觉算法来降低噪声、去除背景噪音、将注意力集中在感兴趣区域，并检测到抓取形式的变化。 系统中采用的一些算法包括双边图像滤波器、最小二乘多项式拟合、Canny边缘检测、K-Means聚类、高斯混合基于背景/前景分割、Hough圆圈变换和Hough线变换。 检测结果经过滤波和突出以向船员提供有关是否存在抓取和需要维护行动的指示。 为了使操作员能容易地重新定义关注区域并调整方法，设计了一个用户界面。 算法在船上的录像带上进行了验证，能够准确地检测到抓取，且假阳性率较低。
URL
https://arxiv.org/abs/2312.02320
