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\begin{document}

\title{Efficient Pavement Distress Classification via Deep Patch Soft Selective
Learning and Knowledge Distillation}



\author[1]{Shizheng Zhang}%
\author[2]{Wenhao Tang}%
\author[2]{Jing Wang}%
\author[2]{Sheng Huang}%
\affil[1]{Zhengzhou University of Light Industry}%
\affil[2]{Chongqing University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
Pavement distress classification is a vital step for automatic pavement
inspection and maintenance. Recently, patch-based approaches have
achieved promising performances and thus extensive attention in this
field. However, these methods simply assume that all patches contribute
equally to the distress classification, leading to weakly discriminating
abilities of models. Moreover, their tedious processes also leads to a
low efficiency in inference. In this letter, we present a novel
patch-based pavement distress classification approach named Deep Patch
Soft Selective Learning (DPS\$\^{}2\$L), which addresses these issues.
Similar to other patch-based approaches, DPS\$\^{}2\$L partitions the
pavement images into patches and aggregates the patch features to
accomplish the task. To address the first issue, we introduce a succinct
Soft Patch Feature Selection Network (SPFSN) to assess the importance of
each patch to the distress classification with a score based on its
feature. These scores will be considered as patch-wise weights for
feature aggregation. In such a manner, the most discriminative patches
are selected in a soft way, and thereby benefit the final
classification. To address the inference efficiency issue, knowledge
distillation is leveraged to transfer the classification knowledge from
DPS\$\^{}2\$L to the image-based approaches, such as EfficientNet-B3.
This distilled model enables incorporating both the advantages of
patch-based approaches in classification performance and the advantages
of image-based approaches in inference efficiency. Extensive experiments
on a large-scale pavement image dataset named CQU-BPDD demonstrates the
superiority of our methods over baselines regardless of performance or
efficiency.%
\end{abstract}%



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