The DOI matches its Crossref record.
Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Techniques and Deep Learning Model
Open accessAbstract
In this paper we present a comparison between standard computer vision techniques and Deep Learning approach for automatic metal corrosion (rust) detection.For the classic approach, a classification based on the number of pixels containing specific red components has been utilized.The code written in Python used OpenCV libraries to compute and categorize the images.For the Deep Learning approach, we chose Caffe, a powerful framework developed at "Berkeley Vision and Learning Center" (BVLC).The test has been performed by classifying images and calculating the total accuracy for the two different approaches.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The use of AI is not compulsory in computer vision techniques such as how Petricca et al. [103] can perform rust detection based on the number of pixels containing certain red components.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [103]
Citations
Cited by
Sorted by citations
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Metadata
- Authors
- Petricca, L., Moss, T., Figueroa, G., & Broen, S.
- Corresponding author
- Petricca, Luca
- Venue
- Computer Science & Information Technology ( CS & IT )
- Published
- 2016-05-21
- Pages
- 91-99
- OpenAlex
- W2400231982
- Cited by
- 94
- References
- 0
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 14 · 2023: 12 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 7 · 2018: 5 · 2017: 2
- FWCI
- 7.76
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Funders
- Statens vegvesen
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- rust detection, computer vision, deep learning, image classification, pixel classification, Caffe, OpenCV, detection method comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Techniques and Deep Learning Model. https://app.cochat.ai/papers/10.5121/csit.2016.60608
- The paper
- Petricca, L., Moss, T., Figueroa, G., & Broen, S. (2016). Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Techniques and Deep Learning Model. Computer Science & Information Technology ( CS & IT ). https://doi.org/10.5121/csit.2016.60608
