Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images
Abstract
:1. Introduction
- Section 2 introduces a system developed to survey the road surface while driving at high speed (100 km/h) with a line-scan camera and an M/V imaging device. It introduces operating equipment and explains the main functions and test results.
- Section 3 describes the adequacy review of pre-setting by surveying newly constructed bridge construction joints with standard computer vision methods applied to the initial system.
- Section 4 describes another detection mechanism that uses machine learning.
- Section 5 concludes the paper and proposes future work.
2. Development of Monitoring Technology for Bridge Expansion Joint Using Line-Scan Cameras
3. Initial Gap Measurement and Evaluation of New Bridge Expansion Joint Device
- (1)
- After examining the joint gap and the average daily temperature on any day,
- (2)
- The joint gap converted into the reference temperature of 15 °C is expressed as a percentage of the capacity of the new joint.
- (a)
- If it is close to 50%, it means that it is installed in the middle of the absolute value of the joint gap. (If it is an expansion and contraction joint with a capacity of 100 mm, it represents 50 mm when the joint gap is 15 °C, which is the reference temperature.)
- (b)
- If it is near 10%, it means that it is installed at a small value of the absolute value of the joint gap. (In the case of an expansion joint with a capacity of 100 mm, it shows 10 mm when the joint gap is 15 °C, which is the reference temperature, so the joint gap is insufficient in summer.)
- (c)
- If it is near 90%, it means that it is installed at a large value of the absolute value of the joint gap. (If it is a 100 mm stretchable joint, it is 90 mm when the joint gap is 15 °C, which is the reference temperature, so the gap is exceeded in winter.)
4. Advanced Identification of Expansion Gap Using Machine Learning
4.1. AI-Based Image Analysis
4.2. Expansion Joint Device Recognition
4.3. Expansion Joint Gap Segmentation
4.4. Gap Distance Analysis Algorithm
4.5. Gap Identification Verification
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Year | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | Recent Average (2011–2018) | Average Year (1981–2010) | Difference | Ratio |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Average Temperature (°C) | 24.0 | 24.7 | 25.4 | 23.6 | 23.7 | 24.8 | 24.5 | 25.4 | 24.5 | 23.6 | +0.8 | 3.8% |
Maximum Temperature (°C) | 36.7 | 38.7 | 39.2 | 37.9 | 38.7 | 39.6 | 39.7 | 41.0 | 38.9 | 37.5 | +1.4 | 3.7% |
Number of Heatwave Days (days) | 14 | 15 | 18 | 6 | 10 | 22 | 14 | 32 | 14.2 | 9.8 | +4.4 | 45% |
Sum (Bridges) | Major causes | ||
Expansion of cement concrete pavement ① | Deformation of backfill ② | ||
276 | 166 (60%) | 110 (40%) |
Safety Management | Sum | Bridges | Tunnels | Box Culverts |
---|---|---|---|---|
Sum (EA) | 27,682 | 15,636 | 2118 | 9928 |
Regular safety inspection 1 | 25,219 | 13,648 | 1643 | 9928 |
Precision safety inspection 2 | 2205 | 1783 | 422 | - |
Precision safety diagnosis | 258 | 205 | 53 | - |
Division | Total | Rail Type | Steel Finger Type | Mono Cell Type |
---|---|---|---|---|
Total | 302 places | 169 places | 128 places | 5 places |
100% | 56% | 42% | 2% | |
lane A | 171 places | 98 places | 71 places | 2 places |
100% | 57% | 42% | 1% | |
lane B | 131 places | 71 places | 57 places | 3 places |
100% | 54% | 44% | 2% |
Installation | Sum | Mono Cell Type | Finger Type | Rail Type | Others |
---|---|---|---|---|---|
EA | 14,784 | 7793 | 1786 | 4228 | 977 |
Prop (%) | 100 | 53 | 12 | 29 | 7 |
Discrimination (%) | Average (%) | Mono Cell Type | Finger Type | Rail Type | Others |
---|---|---|---|---|---|
Accuracy | 67.5 | 71 | 51 | 87 | 61 |
Loss | 32.5 | 29 | 49 | 13 | 39 |
Accuracy by Type of Expansion Joint (%) | Precision | Recall | f1-Score |
---|---|---|---|
Positive (pixels of expansion joints) | 96.61 | 94.38 | 95.49 |
Negative (other pixels) | 99.23 | 99.55 | 99.39 |
Accuracy (%) | Average | Mono Cell Type | Finger Type | Rail Type | Others |
---|---|---|---|---|---|
Conventional algorithm | 67.5 | 71 | 51 | 87 | 61 |
AI algorithm (machine learning) | 95 | 98 | 92 | 99 | 91 |
Improvement rate | ↑27.5 | ↑27 | ↑41 | ↑12 | ↑30 |
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Kim, I.B.; Cho, J.S.; Zi, G.S.; Cho, B.S.; Lee, S.M.; Kim, H.U. Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images. Appl. Syst. Innov. 2021, 4, 94. https://doi.org/10.3390/asi4040094
Kim IB, Cho JS, Zi GS, Cho BS, Lee SM, Kim HU. Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images. Applied System Innovation. 2021; 4(4):94. https://doi.org/10.3390/asi4040094
Chicago/Turabian StyleKim, In Bae, Jun Sang Cho, Goang Seup Zi, Beom Seok Cho, Seon Min Lee, and Hyoung Uk Kim. 2021. "Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images" Applied System Innovation 4, no. 4: 94. https://doi.org/10.3390/asi4040094