Evaluation of Deep Learning-Based Semantic Segmentation Approaches for Autonomous Corrosion Detection on Metallic Surfaces

2019-10-17T19:34:39Z (GMT) by Cheng Qian
The structural defects can lead to serious safety issues and the corrosponding economic losses. In 2013, it was estimated that 2.5 trillion US dollars were spent on corrosion around the world, which was 3.4\% of the global Gross Domestic Product (GDP) (Koch, 2016). Periodical inspection of corrosion and maintenance of steel structures are essential to minimize these losses. Current corrosion inspection guidelines require inspectors to visually assess every critical member within arm's reach. This process is time-consuming, subjective and labor-intensive, and therefore is done only once every two years.

A promising solution is to use a robotic system, such as an Unmanned Aerial Vehicle (UAV), with computer vision techniques to assess corrosion on metallic surfaces. Several studies have been conducted in this area, but the shortcoming is that they cannot quantify the corroded region reliably: some studies only classify whether corrosion exists in the image or not; some only draw a box around corroded region; and some need human-engineered features to identify corrosion. This study aims to address this problem by using deep learning-based semantic segmentation to let the computer capture useful features and find the bounding of corroded regions accurately.

In this study, the performance of four state-of-the-art deep learning techniques for semantic segmentation was investigated for corrosion assessment task,including U-Net, DeepLab, PSPNet, and RefineNet. Six hundred high-resolution images of corroded regions were used to train and test the networks. Ten sets of experiments were performed on each architecture for cross-validation. Since the images were large, two approaches were used to analyze images: 1) subdividing images, 2) down-sampling images. A parametric analysis on these two prepossessing methods was also considered.

Prediction results were evaluated based on intersection over union (IoU), recall and precision scores. Statistical analysis using box chart and Wilcoxon singled ranked test showed that subdivided image dataset gave a better result, while resized images required less time for prediction. Performance of PSPNet outperformed the other three architectures on the subdivided dataset. DeepLab showed the best performance on the resized dataset. It was found Refinenet was not appropriate for corrosion detection task. U-Net was found to be ideal for real-time processing of image while RefineNet did not perform well for corrosion assessment.