PicT：一种用于路面破损分类的小型弱监督视觉Transformer
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification
摘要：
路面破损自动分类有助于提高路面养护效率，降低人力和资源成本。最近有影响力的分支，这项任务划分为补丁的路面图像，并解决这些问题的多实例学习的角度来看。然而，这些方法忽略了斑块之间的相关性，并且在模型优化和推理中效率较低。同时，Swin Transformer能够以其独特的优势解决这两个问题。在Swin Transformer的基础上，提出了一个用于路面破损分类的视觉Transformer（\textbf{PicT}），命名为\textbf{P} avement \textbf{I} mage \textbf{C} classification \textbf{T} transformer。为了更好地利用路面图像在斑块层次上的判别信息，提出了\textit{Patch Labeling Teacher}，利用教师模型在每次迭代过程中从图像标签中动态生成斑块伪标签，并引导模型学习斑块的判别特征. Swin Transformer的宽分类头可能会由于路面图像的破损面积比例小而在特征聚合步骤中稀释破损斑块的区分特征。为了克服这个缺点，我们提出了一个\textit{Patch Refiner}集群补丁到不同的组，只选择最高的痛苦风险组产生一个苗条的头为最终的图像分类。我们评估我们的方法上CQU-BPDD。广泛的结果表明，\textbf{PicT}在检测任务上优于P@R中的$+2.4\%$，在识别任务上优于$F1$中的$+3.9\%$，并且吞吐量为1.8倍，同时使用相同的计算资源享受7倍的训练速度。我们的代码和模型已在\href{https://github.com/DearCaat/PicT}{https://github.com/DearCaat/PicT}上发布。
英文摘要：
Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image into patches and addresses these issues from the perspective of multi-instance learning. However, these methods neglect the correlation between patches and suffer from a low efficiency in the model optimization and inference. Meanwhile, Swin Transformer is able to address both of these issues with its unique strengths. Built upon Swin Transformer, we present a vision Transformer named \textbf{P}avement \textbf{I}mage \textbf{C}lassification \textbf{T}ransformer (\textbf{PicT}) for pavement distress classification. In order to better exploit the discriminative information of pavement images at the patch level, the \textit{Patch Labeling Teacher} is proposed to leverage a teacher model to dynamically generate pseudo labels of patches from image labels during each iteration, and guides the model to learn the discriminative features of patches. The broad classification head of Swin Transformer may dilute the discriminative features of distressed patches in the feature aggregation step due to the small distressed area ratio of the pavement image. To overcome this drawback, we present a \textit{Patch Refiner} to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification. We evaluate our method on CQU-BPDD. Extensive results show that \textbf{PicT} outperforms the second-best performed model by a large margin of $+2.4\%$ in P@R on detection task, $+3.9\%$ in $F1$ on recognition task, and 1.8x throughput, while enjoying 7x faster training speed using the same computing resources. Our codes and models have been released on \href{https://github.com/DearCaat/PicT}{https://github.com/DearCaat/PicT}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
