Airport Runway Crack Detection to Classify and Densify Surface Crack Type
Abhilasha Sharma1, Aryan Bansal2
1Dr. Abhilasha Sharma, Department of Software Engineering, Delhi Technological University, Delhi, India.
2Aryan Bansal, Department of Software Engineering, Delhi Technological University, Delhi, India.
Manuscript received on 02 August 2023 | Revised Manuscript received on 19 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024 | PP: 25-34 | Volume-13 Issue-3, February 2024 | Retrieval Number: 100.1/ijeat.A42731013123 | DOI: 10.35940/ijeat.A4273.13030224
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the extensive development in infrastructure, many airports are being built to satisfy the travel needs of people. The frequent arrival and departure of numerous planes lead to substantial runway damage and related safety concerns. Therefore, the regular maintenance of runways has become an essential task, especially for detecting and classifying cracks due to the intensity heterogeneity of cracks, which results in low realtime performance and time-consuming manual inspections. This paper introduces a new dataset named ARID, comprising eight distinct crack classes. A runway crack detection model based on YOLOv5 and Faster R-CNN has been proposed, which is trained on 8,228 annotated datasets. Then, the model is trained with different parameters to obtain the optimal result. Finally, based on experimental results, the crack detection precision has improved from 83% to 92%, while the recall has increased from 62.8% to 76%.
Keywords: Crack Segmentation, Google API, Pavement Detection, Runway Crack, Runway Distresses Detection.
Scope of the Article: Software Engineering & Its Applications