论文标题
迭代优化的补丁标签推理网络，用于自动路面遇险检测
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
论文作者
论文摘要
我们提出了一个新颖的深度学习框架，称为迭代优化的补丁标签推理网络（IOPLIN），用于自动检测不仅限于特定的路面遇险，例如裂纹和坑洼。 Ioplin只能通过预期最大化启发的补丁标签蒸馏（EMIPLD）策略对图像标签进行迭代训练，并通过从路面图像中推断贴片标签来很好地完成此任务。 Ioplin在最先进的单个分支CNN模型（例如Googlenet和ExcelificeNet）上享有许多理想的属性。它能够在不同的分辨率中处理图像，并充分利用图像信息，尤其是对于高分辨率图像，因为Ioplin从未经修复的图像贴片中提取了视觉特征，而不是整个大小的整个图像。此外，它可以在训练阶段使用任何先前的本地化信息而大致地将路面困扰定位。为了更好地评估我们方法在实践中的有效性，我们构建了一个名为CQU-BPDD的大规模沥青疾病检测数据集，该数据集由60,059个高分辨率路面图像组成，这些数据在不同的地区从不同的地区获取。该数据集的广泛结果表明，在自动路面遇险检测中，Ioplin优于最先进的图像分类方法。 Ioplin的源代码在\ url {https://github.com/dearcaat/ioplin}上发布，并且可以在\ url {https://dearcaat.gith.gith.github.io/cqu-bpdd/}上访问CQU-BPDD数据集。
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/}.
