Shipboard tank corrosion segmentation via deep learning

Jane Berk; Chelsea Mediavilla; Michael Hess; Mark Bilinski; Raymond Provost

doi:10.1117/12.2678327

13 June 2023 Shipboard tank corrosion segmentation via deep learning

Jane Berk, Chelsea Mediavilla, Michael Hess, Mark Bilinski, Raymond Provost

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 125270F (2023) https://doi.org/10.1117/12.2678327
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Accurate and efficient corrosion detection is a difficult but important issue with immediate relevance to maintenance of Naval ships. The current process requires an inspector to physically access the space and perform a very manual visual inspection of the space. Considering the schedules of both the inspector and the ship, coordinating the inspection of hundreds of tanks and voids is not always a straightforward process. There is a significant amount of research into automatic detection of corrosion via computer vision algorithms, but performing pixel level segmentation introduces added difficulty. There are two key reasons for this: the lack of annotated data and the inherent difficulty in the type of problem. In this work, we utilized a combination of annotated data from a different domain and a small hand labeled dataset of panoramic images from our target domain: the inside of empty ship tanks and voids. We trained two High-Resolution Network (HRNet) models for our corrosion detector; the first with a dataset outside our target domain, the second with our hand annotated panoramic tank images. By ensembling our two models, the F1-score increased by about 120% and IOU score by about 176% with respect to the single baseline corrosion detector. The data collection process via LiDAR scanning allows the inspection process to be performed remotely. Additionally, the setup of the detector leads to a natural expansion of the corrosion dataset as panoramas from LiDAR scans are continually fed through the detector and the detections are validated. This allows for the corrosion models to be later retrained for potential improvement in accuracy and robustness.

Conference Presentation

Citation Download Citation

Jane Berk, Chelsea Mediavilla, Michael Hess, Mark Bilinski, and Raymond Provost "Shipboard tank corrosion segmentation via deep learning", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 125270F (13 June 2023); https://doi.org/10.1117/12.2678327

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