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Abstract <p>Compared to contact detection techniques, pavement crack identification with visual images via deep learning algorithms has the advantages of not being limited by the material of detected object, fast speed and low cost. The pavement crack identification performance of typical models or algorithms of transfer learning, encoder-decoder, and generative adversarial networks, were evaluated and compared on public data sets SDNET2018 and Crack Forest Dataset. Transfer learning mainly takes advantage of fine-tuning the architecture-optimized backbones pretrained on large-scale data sets (e.g., ImageNet) to achieve good classification accuracy. Encoder-decoder based algorithms enlarge the contextual receptive field by dilated convolution, taking into account the fact that crack edges, patterns or texture features contribute differently to the identification, which is ignored by transfer learning. Both transfer learning and encoder-decoder rely on accurate crack ground truth annotation. Generative adversarial networks are compatible with other neural network architectures, thus can integrate various frameworks (e.g., transfer learning, encoder-decoder), and algorithms, but the training time is much longer. In patch classification, the fine-tuned transfer-learning models can be equivalent to or even slightly better than the encoder-decoder based algorithms, and the predicting time is faster; In accurate crack location, both encoder-decoder based and generative-adversarial-network based algorithms can achieve pixel-level segmentation. It is expected to realize end-to-end and real-time automatic crack identification on a low computational power platform. Furthermore, a weakly supervised learning framework is proposed, combining transfer-learning and semisupervised generative-adversarial-network. It only needs approximately 10–20% labeled samples of the total to achieve comparable crack classification performance to or even outperform supervised learning algorithms, via fine-tuned backbones and utilizing extra unlabeled samples.</p>

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Comparison of Deep Learning Methods and a Transfer-Learning Semisupervised Generative-Adversarial-Network Combined Framework for Pavement Crack Image Identification

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Kai-liang Lu

摘要

Abstract

Compared to contact detection techniques, pavement crack identification with visual images via deep learning algorithms has the advantages of not being limited by the material of detected object, fast speed and low cost. The pavement crack identification performance of typical models or algorithms of transfer learning, encoder-decoder, and generative adversarial networks, were evaluated and compared on public data sets SDNET2018 and Crack Forest Dataset. Transfer learning mainly takes advantage of fine-tuning the architecture-optimized backbones pretrained on large-scale data sets (e.g., ImageNet) to achieve good classification accuracy. Encoder-decoder based algorithms enlarge the contextual receptive field by dilated convolution, taking into account the fact that crack edges, patterns or texture features contribute differently to the identification, which is ignored by transfer learning. Both transfer learning and encoder-decoder rely on accurate crack ground truth annotation. Generative adversarial networks are compatible with other neural network architectures, thus can integrate various frameworks (e.g., transfer learning, encoder-decoder), and algorithms, but the training time is much longer. In patch classification, the fine-tuned transfer-learning models can be equivalent to or even slightly better than the encoder-decoder based algorithms, and the predicting time is faster; In accurate crack location, both encoder-decoder based and generative-adversarial-network based algorithms can achieve pixel-level segmentation. It is expected to realize end-to-end and real-time automatic crack identification on a low computational power platform. Furthermore, a weakly supervised learning framework is proposed, combining transfer-learning and semisupervised generative-adversarial-network. It only needs approximately 10–20% labeled samples of the total to achieve comparable crack classification performance to or even outperform supervised learning algorithms, via fine-tuned backbones and utilizing extra unlabeled samples.