Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation

TL;DR

This paper presents a novel approach combining deep learning semantic segmentation models with a data fusion scheme to improve pixel-level corrosion detection accuracy on metal structures, aiding structural analysis and pre-fabrication.

Contribution

It introduces a fusion method that combines deep learning segmentation outputs with color segmentation to enhance corrosion detection precision.

Findings

Deep learning models outperform traditional methods in accuracy and efficiency.

The fusion scheme produces more accurate corrosion contours.

Fewer annotated samples are needed for effective detection.

Abstract

Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and pre-fabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep learning models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform better in terms of accuracy and time and require a smaller number of annotated samples compared to other deep models, e.g. CNN. However, the final images derived are still not sufficiently accurate for structural analysis and pre-fabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.