Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and 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 134 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 27 tok/s Pro
GPT-5 High 27 tok/s Pro
GPT-4o 84 tok/s Pro
Kimi K2 192 tok/s Pro
GPT OSS 120B 434 tok/s Pro
Claude Sonnet 4.5 37 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
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

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