{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:45:52Z","timestamp":1772041552356,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.<\/jats:p>","DOI":"10.3390\/s23062954","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T02:01:47Z","timestamp":1678327307000},"page":"2954","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Nirmala","family":"Paramanandham","sequence":"first","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kishore","family":"Rajendiran","sequence":"additional","affiliation":[{"name":"Department of ECE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florence Gnana","family":"Poovathy J","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeshwant Santhanakrishnan","family":"Premanand","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Rochester Institute of Technology, GoLisano College of Computing and Information Sciences, Rochester, NY 14623, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4798-2297","authenticated-orcid":false,"given":"Sanjeeve Raveenthiran","family":"Mallichetty","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pramod","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.9734\/bpi\/castr\/v3\/9438D","article-title":"Research on Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning","volume":"3","author":"Kim","year":"2021","journal-title":"Curr. 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