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Advancing Crack Segmentation Detection:Introducing AAMC-Net Algorithm for Image Crack Analysis
Computer Science and Information Systems, Tome 21 (2024) no. 4.

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This research paper presents an innovative solution to address the challenges of poor detail detection effectiveness and prolonged training time in image segmentation. The proposed approach leverages the Adaptive Attention Multiscale Convolution Network (AAMC-Net), incorporating a multi-scale dilated convolution VGG L network for feature extraction and a deconvolution method for image segmentation. Extensive experiments demonstrate the superior performance of the proposed algorithm concerning intersection over Union (IOU), accuracy, precision, recall, F1, average training efficiency, and segmentation efficiency when compared to several traditional algorithms. On average, the proposed algorithm achieves remarkable improvements of 3.9%, 3.1%, 1.7%, 4.9%, 17.9%, 14.8% ,and 20.2% in these metrics. Moreover, the enhanced algorithm exhibits notable advantages in detail processing and real-time image segmentation detection.
Keywords: Image crack segmentation, Convolutional neural network,VGG L, Attention mechanism 
@article{CSIS_2024_21_4_a13,
     author = {WANG Xiaofang and LIU Chenfang and Hou Junliang and Zhou Liang},
     title = {Advancing {Crack} {Segmentation} {Detection:Introducing} {AAMC-Net} {Algorithm} for {Image} {Crack} {Analysis}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {4},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a13/}
}
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JO  - Computer Science and Information Systems
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WANG Xiaofang; LIU Chenfang; Hou Junliang; Zhou Liang. Advancing Crack Segmentation Detection:Introducing AAMC-Net Algorithm for Image Crack Analysis. Computer Science and Information Systems, Tome 21 (2024) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a13/