A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion
In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy...
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MDPI AG
2025-07-01
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| author | Yadong Yao Yurui Zhang Zai Liu Heming Yuan |
| author_facet | Yadong Yao Yurui Zhang Zai Liu Heming Yuan |
| author_sort | Yadong Yao |
| collection | DOAJ |
| description | In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>c</mi></semantics></math></inline-formula> = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. To enhance accuracy, connected domain labeling combined with a circularity threshold is introduced to differentiate linear cracks from granular noise. Furthermore, a 5 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>×</mo></semantics></math></inline-formula> 5 neighborhood search strategy, based on crack pixel amplitude, is designed to restore the continuity of fragmented cracks. Experimental results on the Concrete Crack and SDNET2018 datasets demonstrate that the proposed algorithm achieves an accuracy of 0.885 and a recall rate of 0.891, outperforming DeepLabv3+ by 4.2%. Notably, with a processing time of only 0.8 s per image, the algorithm balances high accuracy with real-time efficiency, effectively addressing challenges, such as missed fine cracks and misjudged broken cracks in noisy environments by integrating geometric features and pixel distribution characteristics. This study provides an efficient unsupervised solution for bridge damage detection. |
| format | Article |
| id | doaj-art-8c1bc4dfd943457e9fd1672e394678b3 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-8c1bc4dfd943457e9fd1672e394678b32025-08-20T02:47:05ZengMDPI AGSensors1424-82202025-07-012514439910.3390/s25144399A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature FusionYadong Yao0Yurui Zhang1Zai Liu2Heming Yuan3Institute of Transportation, Inner Mongolia University, Hohhot 010070, ChinaInstitute of Transportation, Inner Mongolia University, Hohhot 010070, ChinaInstitute of Transportation, Inner Mongolia University, Hohhot 010070, ChinaInstitute of Transportation, Inner Mongolia University, Hohhot 010070, ChinaIn response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>c</mi></semantics></math></inline-formula> = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. To enhance accuracy, connected domain labeling combined with a circularity threshold is introduced to differentiate linear cracks from granular noise. Furthermore, a 5 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>×</mo></semantics></math></inline-formula> 5 neighborhood search strategy, based on crack pixel amplitude, is designed to restore the continuity of fragmented cracks. Experimental results on the Concrete Crack and SDNET2018 datasets demonstrate that the proposed algorithm achieves an accuracy of 0.885 and a recall rate of 0.891, outperforming DeepLabv3+ by 4.2%. Notably, with a processing time of only 0.8 s per image, the algorithm balances high accuracy with real-time efficiency, effectively addressing challenges, such as missed fine cracks and misjudged broken cracks in noisy environments by integrating geometric features and pixel distribution characteristics. This study provides an efficient unsupervised solution for bridge damage detection.https://www.mdpi.com/1424-8220/25/14/4399fuzzy C-means clusteringbridge crack detectionmulti-feature fusionconnected domain labelingcircularity thresholdunsupervised detection |
| spellingShingle | Yadong Yao Yurui Zhang Zai Liu Heming Yuan A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion Sensors fuzzy C-means clustering bridge crack detection multi-feature fusion connected domain labeling circularity threshold unsupervised detection |
| title | A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion |
| title_full | A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion |
| title_fullStr | A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion |
| title_full_unstemmed | A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion |
| title_short | A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion |
| title_sort | bridge crack segmentation algorithm based on fuzzy c means clustering and feature fusion |
| topic | fuzzy C-means clustering bridge crack detection multi-feature fusion connected domain labeling circularity threshold unsupervised detection |
| url | https://www.mdpi.com/1424-8220/25/14/4399 |
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