An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

TL;DR

This paper introduces IOPLIN, a deep learning framework that automatically detects pavement distress in images by inferring patch labels iteratively, handling various resolutions, and localizing damages without prior localization data.

Contribution

The paper proposes IOPLIN, a novel iterative deep learning method for pavement distress detection that works with only image labels and localizes damages without prior localization information.

Findings

IOPLIN outperforms state-of-the-art CNN models in pavement distress detection.

It effectively handles high-resolution images and different resolutions.

The method achieves accurate localization of pavement damages without prior localization data.

Abstract

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/}.