Sequential concrete crack segmentation using deep fully convolutional neural networks and data fusion
Bibliographic record
Abstract
Algorithms that interpret images to locate surface defects, such as cracks, play a key role in an automated inspection system. That is the reason the success of convolutional neural networks (CNNs) in image object detection persuaded researchers to apply deep CNNs for visual surface crack detection. Among various deep learning architectures, encoder-decoder architectures with fully convolutional networks (FCNs) are powerful tools for automatically segmenting inspection images and detecting crack maps. In this study the U-Net architecture, as a particular FCN, is trained using the available concrete crack datasets. The trained network is then employed to detect crack maps in a sequence of images taken from a concrete beam-column specimen under a cyclic load test. To enhance performance of the crack segmentation, instead of treating each image in the sequence independently, the detection results of the next stages of the experiment are used to determine the crack map at the current stage. By leveraging the fact that cracks propagate sequentially, a data fusion technique is proposed that updates crack maps by considering the outcome of the next steps. To realize this method, reference points on images are utilized to estimate the deformation of the structural members. The deformation information is then used to project the previously detected crack maps onto the current image. This makes it possible to aggregate current and future detections and achieve higher accuracy. The framework laid out in this study provides tools to filter out false positives and recover missed detections. © 2021 SPIE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".