Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 49 tok/s
Gemini 2.5 Pro 53 tok/s Pro
GPT-5 Medium 19 tok/s Pro
GPT-5 High 16 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 172 tok/s Pro
GPT OSS 120B 472 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges (2403.17725v1)

Published 26 Mar 2024 in cs.CV and eess.IV

Abstract: Automating the current bridge visual inspection practices using drones and image processing techniques is a prominent way to make these inspections more effective, robust, and less expensive. In this paper, we investigate the development of a novel deep-learning method for the detection of fatigue cracks in high-resolution images of steel bridges. First, we present a novel and challenging dataset comprising of images of cracks in steel bridges. Secondly, we integrate the ConvNext neural network with a previous state-of-the-art encoder-decoder network for crack segmentation. We study and report, the effects of the use of background patches on the network performance when applied to high-resolution images of cracks in steel bridges. Finally, we introduce a loss function that allows the use of more background patches for the training process, which yields a significant reduction in false positive rates.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (82)
  1. doi:https://doi.org/10.1016/j.jtte.2021.12.003.
  2. doi:https://doi.org/10.1080/15732479.2020.1832539.
  3. doi:https://doi.org/10.1061/(ASCE)BE.1943-5592.0001507.
  4. doi:https://doi.org/10.13023/KTC.RR.2016.26.
  5. doi:https://doi.org/10.3390/app11209757.
  6. doi:https://doi.org/10.3390/s20143954.
  7. doi:https://doi.org/10.1109/LRA.2019.2895880.
  8. doi:https://doi.org/10.3390/drones6110355.
  9. doi:https://doi.org/10.3390/app12031374.
  10. doi:https://doi.org/10.3390/buildings12040432.
  11. doi:10.1080/10298436.2018.1485917.
  12. doi:https://doi.org/10.1016/j.conbuildmat.2022.129659.
  13. doi:https://doi.org/10.3390/su14031825.
  14. doi:https://doi.org/10.1016/j.jtte.2022.11.003.
  15. doi:https://doi.org/10.3390/s19194251.
  16. doi:https://doi.org/10.1109/TIP.2018.2878966.
  17. doi:https://doi.org/10.1109/JSEN.2021.3089718.
  18. doi:https://doi.org/10.1109/JSEN.2019.2934897.
  19. doi:https://doi.org/10.1016/j.knosys.2022.108338.
  20. doi:https://doi.org/10.1109/TITS.2021.3106647.
  21. doi:https://doi.org/10.1109/TIM.2021.3075022.
  22. doi:https://doi.org/10.1109/TII.2020.3033170.
  23. doi:https://doi.org/10.1111/mice.12477.
  24. doi:https://doi.org/10.1111/mice.12844.
  25. doi:https://doi.org/10.1016/j.rineng.2023.101267.
  26. doi:https://doi.org/10.1061/(ASCE)CP.1943-5487.0000883.
  27. doi:https://doi.org/10.1109/ACCESS.2020.2980086.
  28. doi:https://doi.org/10.1109/TITS.2020.2990703.
  29. doi:https://doi.org/10.48550/arXiv.2010.11929.
  30. doi:https://doi.org/10.48550/arXiv.2304.02643.
  31. doi:https://doi.org/10.1016/j.autcon.2022.104646.
  32. doi:https://doi.org/10.1007/s00521-023-08277-7.
  33. doi:https://doi.org/10.1016/j.autcon.2022.104275.
  34. doi:https://doi.org/10.48550/arXiv.2105.15203.
  35. doi:https://doi.org/10.1007/s00138-020-01098-x.
  36. doi:https://doi.org/10.1007/s10851-023-01147-w.
  37. doi:https://doi.org/10.1016/j.dsp.2020.102907.
  38. doi:https://doi.org/10.1177/14759217211006485.
  39. doi:https://doi.org/10.1177/1475921718764873.
  40. doi:https://doi.org/10.1007/s13349-021-00537-1.
  41. doi:https://doi.org/10.21595/mrcm.2021.22032.
  42. doi:https://doi.org/10.3390/s21124135.
  43. doi:https://doi.org/10.1016/j.measurement.2022.111805.
  44. doi:https://doi.org/10.1016/j.jobe.2022.104098.
  45. doi:https://doi.org/10.1111/mice.12918.
  46. doi:https://doi.org/10.1007/s11263-021-01515-2.
  47. doi:https://doi.org/10.1016/j.autcon.2022.104299.
  48. doi:10.4121/6162a9b6-2a20-4600-8207-e9dcd53a264a.
  49. doi:https://doi.org/10.1109/5.726791.
  50. doi:https://doi.org/10.1145/3065386.
  51. doi:https://doi.org/10.48550/arXiv.1701.04128.
  52. doi:https://doi.org/10.1109/TITS.2016.2552248.
  53. doi:https://doi.org/10.48550/arXiv.1409.1556.
  54. doi:https://doi.org/10.1007/s11263-015-0816-y.
  55. doi:https://doi.org/10.48550/arXiv.1411.1792.
  56. doi:https://doi.org/10.1007/s00521-021-06279-x.
  57. doi:https://doi.org/10.48550/arXiv.1905.11946.
  58. doi:https://doi.org/10.1177/14759217211053776.
  59. doi:https://doi.org/10.1023/B:VISI.0000022288.19776.77.
  60. doi:https://doi.org/10.48550/arXiv.1907.02248.
  61. doi:https://doi.org/10.1007/s10851-018-0795-z.
  62. doi:https://doi.org/10.48550/arXiv.1711.05101.
  63. doi:https://doi.org/10.1090/S0033-569X-10-01172-0.
  64. doi:https://doi.org/10.1007/s10851-023-01170-x.
  65. doi:https://doi.org/10.1016/j.autcon.2022.104678.
  66. doi:https://doi.org/10.1109/TITS.2019.2910595.
  67. doi:https://doi.org/10.1016/j.neucom.2019.01.036.
  68. doi:https://doi.org/10.1111/mice.12412.
  69. doi:https://doi.org/10.1109/TITS.2015.2477675.
  70. doi:https://doi.org/10.1016/j.conbuildmat.2020.119397.
  71. doi:https://doi.org/10.1109/ACCESS.2018.2829347.
  72. doi:https://doi.org/10.3390/s20092557.
  73. doi:https://doi.org/10.1016/j.neucom.2022.01.051.
  74. doi:https://doi.org/10.1109/JAS.2023.123447.
  75. doi:https://doi.org/10.1016/j.autcon.2022.104436.
  76. doi:https://doi.org/10.1111/mice.12881.
  77. doi:https://doi.org/10.48550/arXiv.1802.02208.
  78. doi:https://doi.org/10.3390/coatings10020152.
  79. doi:https://doi.org/10.3390/s21092902.
  80. doi:https://doi.org/10.1016/j.engappai.2023.106142.
  81. doi:https://doi.org/10.1177/14759217221089571.
  82. doi:https://doi.org/10.13053/cys-23-2-3047.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

Explore Trending Papers

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube