We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}, and the CQU-BPDD dataset is able to be accessed on \url{https://dearcaat.github.io/CQU-BPDD/}.
翻译:我们提出了一个叫“超优化补丁标签”的深层次学习框架,名为“超优化补丁标签标签网络”,用于自动检测不仅限于裂缝和坑洞等特定问题的各种路面困难。 IOPLIN只能通过“期待-最大化”激励的 Patch Label 蒸馏(EMIPLD) 战略进行图像标签的迭代培训,通过从铺面图像中推断补丁标签来完成这项任务。 IOPLIN在GoogLeNet 和“高效网络”等最先进的单一CNN模型上享有许多可取的特性。它能够处理不同分辨率的图像,并充分利用图像信息,特别是高分辨率的图像。因为IPLIN从未经修改的图像补丁中提取的图像特征,而不是整幅整幅图像。此外,它可以在培训阶段不使用任何先前的本地化信息而将路面难易读。为了更好地评估我们实践的方法的有效性,我们在Googlib Stabious PavinNet和“高效”网络网络模型模型中,我们可以在不同分辨率的源库中构建一个大规模BSload-de-Opint-OintimalSudalationalationalation QQQQQal 数据显示不同的图像检测数据区域,这是CDDDSetdrealde drealde dregatedalationalationaldealationalde drogetdaldaldaldaldaldal 。