Abstract
To prevent damage caused by cracks, accurate segmentation of cracks is necessary. Deep learning models are commonly employed to achieve this goal, typically consisting of data-driven neural networks that are trained to determine classification probability for each pixel. However, these models often ignore the optimization of the binarization function, which maps the probability distribution of each pixel to a specific class. Typically, a fixed threshold of 0.5 is used, disregarding the sensitivity of crack data to the threshold. As a result, segmentation accuracy is compromised. To address this issue, we propose a multi-objective optimization method that incorporates both the conventional segmentation model’s objective function and a dynamic threshold-based binarization objective function. By doing so, we aim to improve the accuracy of the segmentation results. Specifically, we introduce a dynamic thresholding branch (DTB) to our approach, which performs a regression task to determine the optimal threshold for each crack image at the image level. This optimal threshold is then utilized in the binarization function to optimize the dynamic thresholding-based binarization objective function. We have conducted experiments to validate the effectiveness of our multi-objective optimization approach with DTB on several well-known crack segmentation models. Additionally, we have evaluated its performance on various crack segmentation datasets. The results indicate that our approach can improve the accuracy of crack segmentation.
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Acknowledgements
This work is funded in part by the National Natural Science Foundation of China under Grants No. 62176029. This work also is supported in part by the Chongqing Technology Innovation and Application Development Special under Grants CSTB2022TIAD-KPX0206. We express our sincere gratitude to the above funding agencies. We also acknowledge the computational support from the Chongqing Artificial Intelligence Innovation Center.
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This study followed the ethical guidelines of the National Natural Science Foundation of China and the ethical review standards of the Chongqing Artificial Intelligence Innovation Center. This study did not involve any human or animal participants, nor did it use any sensitive or private data. The purpose of this study was to promote scientific advancement and social welfare in the field of artificial intelligence, and it did not cause any harm or adverse effects to any individual or group. All authors of this study are responsible for the content of this paper, and declare that they have no conflicts of interest or academic misconduct.
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Lei, Q., Zhong, J., Wang, C., Xia, Y., Zhou, Y. (2023). Dynamic Thresholding for Accurate Crack Segmentation Using Multi-objective Optimization. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_23
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