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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2312.02320 |
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| _version_ | 1866916374804168704 |
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| author | Goodman, Ari Shevach, Glenn Zabriskie, Sean Thajudeen, Chris |
| author_facet | Goodman, Ari Shevach, Glenn Zabriskie, Sean Thajudeen, Chris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02320 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cable Slack Detection for Arresting Gear Application using Machine Vision Goodman, Ari Shevach, Glenn Zabriskie, Sean Thajudeen, Chris Computer Vision and Pattern Recognition 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. |
| title | Cable Slack Detection for Arresting Gear Application using Machine Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.02320 |