Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases not just limited to the specific ones, such as crack and pothole. IOPLIN can be iteratively trained with only the image label via using Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplishes 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 any resolution of image and sufficiently utilize image information particularly for the high-resolution ones. Moreover, it can roughly localize the pavement distress without using any prior localization information in 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 consists of 60059 high-resolution pavement images, which are acquired from different areas at different time. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classificaiton approaches in automatic pavement disease detection.