ABSTRACT
Bridge condition assessment is vital for ensuring the safety and reliability of transportation infrastructure. Traditional inspection methods have limitations in terms of efficiency and coverage, motivating the exploration of innovative technologies. Our methodology introduces a robust image stitching approach, which enables the creation of full-scale views of target structures by seamlessly combining a vast number of single images from different vantage points. To facilitate efficient defect detection, segmentation techniques are used to automatically identify target structural surfaces and associated defects. This streamlined process significantly reduces the need for manual intervention, making the inspection more efficient and reliable. The advantages of the full-scale view-based approach lie in its ability to cover large bridge span with a single panoramic image, thus reducing the inspection time and enhancing safety by minimizing the need for inspectors to work in hazardous locations. The paper concludes by highlighting the significant impact of full-scale image-based assessment on bridge inspection, offering a cost-effective and reliable solution for infrastructure management. By leveraging advanced computer vision and deep learning techniques, the proposed approach represents a promising step towards ensuring the long-term integrity and safety of the transportation infrastructure.
