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<p>Automatic detection of asphalt pavement cracks on digital images is crucial for pavement condition assessment. However, pavement images are prone to blurring due to noise generated by imaging devices, which undermines the ability of semantic segmentation models to extract features. Current denoising algorithms show limited capability in handling real noise in pavement images, while most semantic segmentation models struggle to detect fine cracks effectively and accurately. Aiming to tackle these challenges, we propose an <i>integrated denoising and segmentation network</i> (IDSNet), which integrates adaptive denoising filters into a semantic segmentation network. The proposed IDSNet comprises the multi-filter denoising module, hyperparameter prediction module, and semantic segmentation module. The multi-filter denoising module is introduced to suppress background noises and shadow interference. The hyperparameter prediction module is employed to tune the filter parameters based on the loss of the semantic segmentation module. Additionally, a spatial and channel reconstruction convolution operation is embedded into the Attention U-Net to enhance its ability to extract features of fine cracks from context information. Experimental results demonstrate that IDSNet outperforms other state-of-the-art segmentation models in detecting asphalt pavement cracks, achieving a mIoU of 55.10% and an F1-Score of 60.86% on the private Asphalt Pavement Crack Denoising Image Dataset (APCD dataset). Compared to the traditional Attention U-Net, IDSNet demonstrates improvements of 6.88% in mIoU and 6.51% in F1-Score, respectively. Accordingly, IDSNet will assist in image denoising and intelligent crack detection for asphalt pavement.</p>

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An Improved Crack Detection Method on Asphalt Pavement via Integrating Adaptive Denoising Filters into Semantic Segmentation Network

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Baoxian Li,
  • Zhipeng He,
  • Qianyu Chen,
  • Guangtao Yang,
  • Hongbin Guo

摘要

Automatic detection of asphalt pavement cracks on digital images is crucial for pavement condition assessment. However, pavement images are prone to blurring due to noise generated by imaging devices, which undermines the ability of semantic segmentation models to extract features. Current denoising algorithms show limited capability in handling real noise in pavement images, while most semantic segmentation models struggle to detect fine cracks effectively and accurately. Aiming to tackle these challenges, we propose an integrated denoising and segmentation network (IDSNet), which integrates adaptive denoising filters into a semantic segmentation network. The proposed IDSNet comprises the multi-filter denoising module, hyperparameter prediction module, and semantic segmentation module. The multi-filter denoising module is introduced to suppress background noises and shadow interference. The hyperparameter prediction module is employed to tune the filter parameters based on the loss of the semantic segmentation module. Additionally, a spatial and channel reconstruction convolution operation is embedded into the Attention U-Net to enhance its ability to extract features of fine cracks from context information. Experimental results demonstrate that IDSNet outperforms other state-of-the-art segmentation models in detecting asphalt pavement cracks, achieving a mIoU of 55.10% and an F1-Score of 60.86% on the private Asphalt Pavement Crack Denoising Image Dataset (APCD dataset). Compared to the traditional Attention U-Net, IDSNet demonstrates improvements of 6.88% in mIoU and 6.51% in F1-Score, respectively. Accordingly, IDSNet will assist in image denoising and intelligent crack detection for asphalt pavement.