mlsanityData to 7 Oct 2026

Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning

arXiv 2203.09580 ↗First posted 17 Mar 2022cs.CV cs.AIcited by 2

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Abstract (arXiv)

Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is highly challenging because of the high level of variation in vessel type, paint surface, coatings, lighting condition, weather condition, paint colors, areas of the vessel, and time in service. We present a novel deep learning-based pipeline to detect and evaluate the percentage of corrosion, fouling, and delamination on the vessel surface from normal photographs. We propose a multi-stage image processing framework, including ship section segmentation, defect segmentation, and defect classification, to automatically recognize different types of defects and measure the coverage percentage on the ship surface. Experimental results demonstrate that our proposed pipeline can objectively perform a similar assessment as a qualified inspector.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

22 of 42 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2017Mask R-CNNcited by 7,259
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  8. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  9. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  10. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  11. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  12. 2015Spatial Transformer Networkscited by 1,423
10 more
  1. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  2. 2018Path Aggregation Network for Instance Segmentationcited by 796
  3. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
  4. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  5. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 200
  6. 2019HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentationcited by 107
  7. 2017MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Featurescited by 54
  8. 2019Road Damage Detection Based on Unsupervised Disparity Map Segmentationcited by 24
  9. 2019Improving the Robustness of Capsule Networks to Image Affine Transformationscited by 21
  10. 2019Cosine-similarity penalty to discriminate sound classes in weakly-supervised sound event detectioncited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wildcited by 1
  2. 2024Incorporating simulated spatial context information improves the effectiveness of contrastive learning modelscited by 1