Johannes Flotzinger

2024

1. dacl-challenge: Semantic Segmentation during Visual Bridge Inspections

   Johannes Flotzinger, Philipp J. Rösch, Christian Benz, and 6 more authors

   In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2024

   Abs Bib Code Website

   Civil engineering structures - such as bridges - form an essential component of the transportation infrastructure. A failure of an individual structure can result in enormous damage and costs. The economic costs caused by the closure of a bridge due to congestion can be many times the costs of the bridge itself and its maintenance. Thus, it is mandatory to keep these structures in a safe and operational state. In order to ensure this, they are frequently inspected. However, the current inspection process is error-prone and lengthy. Especially the damage documentation using a hand-drawn sketch causes inconsistencies in the building assessment. On the other hand, recent advancements in hardware enable the deployment of computer vision models for increasing the quality, traceability, and efficiency of structural inspections. Such models are the key element of digitized structural inspections and the basis for automated damage classification, measurement and localization on a pixel-level. Current datasets available for this task suffer from limitations in both size and diversity of classes, raising concerns about their applicability in real-world contexts and their effectiveness as benchmarks. Addressing this problem, we introduced “dacl10k” (damage classification), a diverse dataset designed for multi-label semantic segmentation. Comprising 9,920 images extracted from real-world bridge inspections, “dacl10k” stands out by its comprehensive coverage. It includes 13 damage classes and 6 crucial bridge components pivotal in assessing structures and guiding decisions on restoration, traffic restrictions, and bridge closures. To accelerate progress in baseline development, we organized the “dacl-challenge”, inviting enthusiasts in damage recognition to vie for training the best performing model on the “dacl10k” dataset. The competition is at the core of the “1st Workshop on Vision-Based Structural Inspections in Civil Engineering”, hosted at WACV 2024. In total, 23 participants registered for the challenge, with eight achieving a performance superior to our baseline. The best result shows a mean intersection-over-union of 51%. This paper delineates the challenge’s structure, introduces the dataset utilized, presents the achieved outcomes, and outlines prospective avenues for further exploration in this domain.

   @inproceedings{10495681,
     author = {Flotzinger, Johannes and Rösch, Philipp J. and Benz, Christian and Ahmad, Muneer and Cankaya, Murat and Mayer, Helmut and Rodehorst, Volker and Oswald, Norbert and Braml, Thomas},
     booktitle = {2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)},
     title = {dacl-challenge: Semantic Segmentation during Visual Bridge Inspections},
     year = {2024},
     volume = {},
     number = {},
     pages = {716-725},
     keywords = {Bridges;Computer vision;Visualization;Costs;Conferences;Computational modeling;Biological system modeling},
     doi = {10.1109/WACVW60836.2024.00084},
   }

2. dacl10k: Benchmark for Semantic Bridge Damage Segmentation

   Johannes Flotzinger, Philipp J. Rösch, and Thomas Braml

   In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

   Abs Bib Code Website

   Reliably identifying reinforced concrete defects (RCDs) plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.

   @inproceedings{10483794,
     author = {Flotzinger, Johannes and Rösch, Philipp J. and Braml, Thomas},
     booktitle = {2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
     title = {dacl10k: Benchmark for Semantic Bridge Damage Segmentation},
     year = {2024},
     volume = {},
     number = {},
     pages = {8611-8620},
     keywords = {Bridges;Semantic segmentation;Semantics;Telecommunication traffic;Inspection;Benchmark testing;Concrete;Applications;Structural engineering / civil engineering;Algorithms;Datasets and evaluations;Algorithms;Image recognition and understanding},
     doi = {10.1109/WACV57701.2024.00843},
   }