{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:09:03Z","timestamp":1760609343520,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In view of the lack of image enhancement processing in the traditional methods in image preprocessing, which leads to a long detection time for internal cracks in the image and poor visual effects, an intelligent detection method for internal cracks in aircraft landing gear images under multimedia processing is proposed. A spatial index structure is established based on the multimedia database, and the aircraft landing gear images in the structure are enhanced and denoised. Image segmentation is performed according to the preprocessing results, the crack foreground and the road surface background in the image are separated, and the threshold value of each image is calculated. The threshold segmentation result is used to distinguish which pixels are the areas where the cracks may exist and which pixels belong to the image background, and the judgment result realizes crack detection. The experimental results show that the crack detection time of the proposed method is shorter, the visual effect of the detection results is better, and the average of the expert score reaches 93.6 points, which verifies the effectiveness of the proposed method from both the subjective and objective aspects.<\/jats:p>","DOI":"10.3390\/sym13050778","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T10:55:04Z","timestamp":1619780104000},"page":"778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing"],"prefix":"10.3390","volume":"13","author":[{"given":"Renfei","family":"Luo","sequence":"first","affiliation":[{"name":"International Business School, Guangdong University of Finance &amp; Economics, Guangzhou 510320, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1104-2068","authenticated-orcid":false,"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102959","DOI":"10.1016\/j.autcon.2019.102959","article-title":"Self-reconfigurable facade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks","volume":"108","author":"Kouzehgar","year":"2019","journal-title":"Autom. 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