Bridges are conventionally constructed using reinforced concrete or steel. While steel structures are lightweight and can be built quickly, they are susceptible to corrosion and elastic fatigue. Environmental factors such as vehicle exhaust, industrial pollutants, and humid climates significantly reduce the service life of steel bridges . Regular inspection and maintenance are crucial for ensuring safety; however, accessing steel decks beneath bridges, especially those spanning rivers or valleys, poses significant challenges. Current inspection methods involve professional inspectors conducting visual assessments, which are subjective, dangerous, and often incomplete due to inaccessible areas.
Bridges require regular inspections throughout their service life to ensure structural safety and functionality. With its extensive bridge infrastructure, Taiwan faces significant challenges in performing these inspections and maintaining bridge conditions. Corrosion, particularly in steel bridges north of Central Taiwan, has been identified as the primary form of deterioration. The current approach, involving manual annotation of corrosion areas, is not only time-consuming and labor-intensive but also prohibitively expensive, with market rates for manual image annotation reaching NT$300,000 (∼USD 10,000) per bridge (Fig. 1). This financial burden underscores the urgent need for more efficient and cost-effective solutions.
Effective bridge maintenance hinges on accurately assessing the severity of corrosion, as not all corrosion is equally damaging. A systematic grading of corrosion allows bridge management authorities to categorize deterioration levels, prioritize repair needs, and allocate resources more effectively. With such a system, maintenance strategies may be aligned, leading to necessary repairs or neglecting critical areas requiring immediate attention.
Recent studies have focused on leveraging computer vision and deep learning techniques to identify bridge deterioration areas. However, only some have explored automatic annotation modules for bridge images. Accurate annotation is crucial for developing automated bridge inspection systems, as the performance of Artificial Intelligence (AI) models depends on the availability of annotated training datasets; producing a model that achieves accurate predictions and is generalizable necessitates the availability of sufficient data. This study addresses this gap by developing an automatic annotation module to efficiently identify corrosion deterioration on steel bridge decks.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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