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Building Inspection Toolkit: Unified Evaluation And Strong Baselines For Bridge Damage Recognition

In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on...

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Main Authors: Flotzinger, Johannes, Rosch, Philipp J., Oswald, Norbert, Braml, Thomas
Format: Conference Proceeding
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creator Flotzinger, Johannes
Rosch, Philipp J.
Oswald, Norbert
Braml, Thomas
description In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsistent dataset splitting with the absence of consistent metrics on the other hand. This leads to incomparable results. Therefore, we introduce the building inspection toolkit - bikit - which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community. As a starting point we provide strong baselines utilizing extensive hyperparameter search using three transfer learning approaches for state-of-the-art algorithms. The toolkit 1 and the leaderboard 2 are available online.
doi_str_mv 10.1109/ICIP46576.2022.9897743
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subjects Adaptation models
Bridges
building damage recognition
Buildings
Companies
deep learning
Image recognition
Measurement
Transfer learning
title Building Inspection Toolkit: Unified Evaluation And Strong Baselines For Bridge Damage Recognition
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