Automatic Defect Detection of Chain Link Fences Using Artificial Intelligence
Bibliographic record
Abstract
Chain link fences are used everywhere, whether it be for residential, commercial, or industrial use. These fences are used for security, safety, and boundary delineation. Once they are damaged, it is essential to replace or repair the fences as soon as possible to protect properties from unauthorized access. Typically, this process requires manual inspection to detect all damages and categorize them into specific categories, which is labor-intensive and prone to human error. In this paper, we propose an automated inspection process using anomaly detection techniques through computer vision, exploring various algorithms to address the task of detecting a variety of damages in chain link fences. Our approach includes evaluating several semantic segmentation models and analyzing the impact of design criteria such as edge detection, pre-processing, and post-processing. The results show that our proposed model utilizing the combination of IS-NET from Dichotomous Image Segmentation and Masked Autoencoder (MAE) outperforms the current approaches and effectively detects most of the defects in chain link fence images. This approach could significantly reduce human subjectivity, while minimizing the time, risk, cost, and safety aspects associated with manual inspection process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".