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Deep Convolutional Neural Networks for Image Reconstruction and Damage Recognition in UAV Bridge Inspection

Shun Wang

Shun Wang

Shanghai Municipal Highway Engineering Inspection Co., Ltd.Shanghai, China
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Wang, Shun
 et 
Mingwei Sun

Mingwei Sun

Jiangbei District Highway and Transportation Management CenterNingbo, China
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Sun, Mingwei
  
26 mars 2025
Deep Convolutional Neural Networks for Image Reconstruction and Damage Recognition in UAV Bridge Inspection's Cover Image
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Publié en ligne: 26 mars 2025
Reçu: 27 oct. 2024
Accepté: 23 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0811
Mots clés
© 2025 Shun Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Basic structure of deep convolutional neural network
Basic structure of deep convolutional neural network

Figure 2.

Overall network framework
Overall network framework

Figure 3.

Comparison of the mean square error results
Comparison of the mean square error results

Figure 4.

Model training loss rate
Model training loss rate

Figure 5.

Model training accuracy
Model training accuracy

Figure 6.

Model training results
Model training results

Figure 7.

Comparison of the real label and forecast label
Comparison of the real label and forecast label

Image reconstruction comparison

Number DCNN BP CNN FNN
Acc Time/s Acc Time/s Acc Time/s Acc Time/s
1 0.976 0.288 0.837 7.772 0.778 13.771 0.754 19.265
2 0.965 0.066 0.747 6.232 0.819 14.257 0.632 24.307
3 0.970 0.071 0.909 7.430 0.779 13.28 0.726 23.820
4 0.985 0.670 0.832 7.237 0.792 15.718 0.639 19.348
5 0.998 0.793 0.789 6.208 0.823 13.643 0.641 18.086
6 0.993 0.318 0.877 7.765 0.795 11.642 0.642 15.660
7 0.998 0.309 0.822 5.572 0.795 12.924 0.703 20.258
8 0.979 0.059 0.768 5.949 0.769 14.637 0.696 24.699
9 0.99 0.590 0.760 7.991 0.790 15.454 0.753 19.772
10 0.985 0.404 0.890 5.503 0.738 14.562 0.625 16.051
11 0.977 0.876 0.745 7.476 0.771 11.004 0.665 16.611
12 0.980 0.161 0.838 6.495 0.742 12.908 0.680 20.618
13 0.999 0.702 0.762 5.825 0.762 15.696 0.735 24.489
14 0.985 0.779 0.812 6.284 0.780 15.526 0.706 17.989
15 0.981 0.456 0.844 5.138 0.702 14.072 0.686 23.111
16 0.963 0.314 0.842 6.920 0.729 14.944 0.678 20.063
17 0.986 0.779 0.906 7.680 0.756 13.074 0.756 20.746
18 0.964 0.203 0.784 7.806 0.766 11.540 0.712 18.323
19 0.971 0.733 0.816 6.618 0.707 12.782 0.714 23.987
20 0.982 0.599 0.873 7.869 0.710 14.328 0.644 24.188
Mean 0.981 0.459 0.823 6.789 0.765 13.788 0.689 20.570

Recognition results based on deflection damage index

Position Damage degree/% Recognition result Recognition rate/% Position Damage degree/% Recognition result Recognition rate
1 0 1 100 6 20 6 100
0 1 100 40 6 100
0 1 100 60 6 100
0 1 100 80 6 100
2 20 2 100 7 20 1 90.82
40 2 100 40 7 100
60 2 100 60 5 91.48
80 2 100 80 8 93.56
3 20 3 100 8 20 1 92.38
40 3 100 40 7 95.66
60 3 100 60 8 100
80 3 100 80 8 100
4 20 1 92.17 9 20 9 100
40 4 100 40 9 100
60 4 100 60 9 100
80 4 100 80 9 100
5 20 1 92.36 10 20 10 100
40 5 100 40 10 100
60 5 100 60 10 100
80 5 100 80 10 100

The bridge image peak signal-to-noise ratio of different methods

Image number Ours A B C
1 45.42 15.91 21.53 13.17
2 55.67 27.47 26.06 16.7
3 46.94 23.74 12.98 29.82
4 49.74 26.93 16.75 24.65
5 47.12 15.44 16.27 12.55
6 51.04 26.50 13.21 27.25
7 58.94 27.31 26.79 27.97
8 48.83 28.95 27.93 13.89
9 46.40 17.68 26.87 26.13
10 48.62 26.79 26.10 16.86
11 53.62 25.11 19.46 12.93
12 52.84 24.73 27.26 14.15
13 56.60 20.35 14.08 18.59
14 59.39 16.52 22.49 17.32
15 48.46 26.57 11.67 22.86
16 51.31 18.35 26.02 26.10
17 55.70 23.85 20.18 27.9
18 55.58 20.57 25.56 28.78
19 46.51 15.25 17.84 19.74
20 46.65 16.01 26.57 28.13

Comparison of SSIM results

Image number Ours A B C
1 0.987 0.777 0.683 0.750
2 0.984 0.770 0.678 0.754
3 0.958 0.788 0.682 0.745
4 0.975 0.753 0.656 0.747
5 0.987 0.764 0.669 0.751
6 0.987 0.766 0.661 0.755
7 0.984 0.790 0.664 0.737
8 0.988 0.782 0.662 0.760
9 0.969 0.780 0.667 0.732
10 0.987 0.769 0.666 0.760
11 0.963 0.773 0.679 0.743
12 0.964 0.774 0.645 0.730
13 0.966 0.764 0.683 0.723
14 0.975 0.786 0.657 0.730
15 0.967 0.763 0.641 0.728
16 0.983 0.775 0.660 0.749
17 0.952 0.777 0.644 0.742
18 0.973 0.769 0.681 0.746
19 0.969 0.755 0.645 0.741
20 0.956 0.759 0.686 0.764

Recognition results based on acceleration damage indicators

Position Damage degree/% Recognition result Recognition rate/% Position Damage degree/% Recognition result Recognition rate
1 0 1 100 6 20 6 100
0 1 100 40 6 100
0 1 100 60 6 100
0 2 99.38 80 2 91.64
2 20 1 93.74 7 20 7 100
40 2 100 40 1 90.39
60 6 90.62 60 7 100
80 3 95.84 80 7 100
3 20 7 90.71 8 20 8 100
40 3 100 40 2 91.26
60 3 100 60 8 100
80 3 100 80 8 100
4 20 4 100 9 20 9 100
40 5 94.76 40 9 100
60 4 100 60 9 100
80 4 100 80 9 100
5 20 2 92.83 10 20 9 98.47
40 3 93.46 40 10 100
60 5 100 60 10 100
80 1 90.17 80 10 100
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