Traffic safety assessment method of the immersed tunnel based on small target visual recognition image
The quality of lighting installation performance has a direct impact on the traffic safety of immersed tunnels. To effectively investigate and judge the traffic safety of immersed tunnels having different lighting installations, a traffic safety assessment method for immersed tunnels based on lighti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2023.1159531/full |
| _version_ | 1827997167483092992 |
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| author | Meng Yang Meng Yang Shanfeng Lu Hao Ding Hao Ding Jianzhong Chen Jianzhong Chen |
| author_facet | Meng Yang Meng Yang Shanfeng Lu Hao Ding Hao Ding Jianzhong Chen Jianzhong Chen |
| author_sort | Meng Yang |
| collection | DOAJ |
| description | The quality of lighting installation performance has a direct impact on the traffic safety of immersed tunnels. To effectively investigate and judge the traffic safety of immersed tunnels having different lighting installations, a traffic safety assessment method for immersed tunnels based on lighting performance degradation was put forward in this study by using big data technology. Numerical simulation was used to simulate the lighting environment in an immersed tunnel under different conditions of lighting performance degradation, conduct the small target recognition test in a physical tunnel, and calculate the traffic safety factor; then, a real-time kinematic assessment model of traffic safety in immersed tunnels was built in combination with the key index factors influencing lighting installations in immersed tunnels. The test results showed that the performance degradation of lighting installations positively correlated with the visual cognition of drivers and passengers. long short-term memory neural network model can effectively assess the traffic safety of immersed tunnels, and the root mean square error (RMSE) and coefficient of determination of the model were separately 1.029 and 0.95, which were superior to the RMSE and coefficient of determination of random forest and recurrent neural network model, and the running time was often less than 1min, complying with the rea; -time assessment requirements; the boundary value of the traffic safety factor of immersed tunnels was 0.6304, and if a value was less than the boundary value, it indicated that the performance of lighting installations was not good and might pose a threat to traffic safety. The research results provided a new perspective for the status assessment of lighting installations in immersed tunnels and also offered a theoretical basis for fine maintenance and repairs of lighting installations. |
| first_indexed | 2024-04-10T05:23:06Z |
| format | Article |
| id | doaj.art-f6481df2745a4354a738728823c342e5 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-10T05:23:06Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-f6481df2745a4354a738728823c342e52023-03-08T05:56:57ZengFrontiers Media S.A.Frontiers in Physics2296-424X2023-03-011110.3389/fphy.2023.11595311159531Traffic safety assessment method of the immersed tunnel based on small target visual recognition imageMeng Yang0Meng Yang1Shanfeng Lu2Hao Ding3Hao Ding4Jianzhong Chen5Jianzhong Chen6China Merchants Chongqing Communications Technology Research and Design Institute Co, Ltd., Chongqing, ChinaNational Engineering Research Center for Highway Tunnel, Chongqing, ChinaGuangxi Xinhengtong Expressway Co, Ltd., Guangxi, ChinaChina Merchants Chongqing Communications Technology Research and Design Institute Co, Ltd., Chongqing, ChinaNational Engineering Research Center for Highway Tunnel, Chongqing, ChinaChina Merchants Chongqing Communications Technology Research and Design Institute Co, Ltd., Chongqing, ChinaNational Engineering Research Center for Highway Tunnel, Chongqing, ChinaThe quality of lighting installation performance has a direct impact on the traffic safety of immersed tunnels. To effectively investigate and judge the traffic safety of immersed tunnels having different lighting installations, a traffic safety assessment method for immersed tunnels based on lighting performance degradation was put forward in this study by using big data technology. Numerical simulation was used to simulate the lighting environment in an immersed tunnel under different conditions of lighting performance degradation, conduct the small target recognition test in a physical tunnel, and calculate the traffic safety factor; then, a real-time kinematic assessment model of traffic safety in immersed tunnels was built in combination with the key index factors influencing lighting installations in immersed tunnels. The test results showed that the performance degradation of lighting installations positively correlated with the visual cognition of drivers and passengers. long short-term memory neural network model can effectively assess the traffic safety of immersed tunnels, and the root mean square error (RMSE) and coefficient of determination of the model were separately 1.029 and 0.95, which were superior to the RMSE and coefficient of determination of random forest and recurrent neural network model, and the running time was often less than 1min, complying with the rea; -time assessment requirements; the boundary value of the traffic safety factor of immersed tunnels was 0.6304, and if a value was less than the boundary value, it indicated that the performance of lighting installations was not good and might pose a threat to traffic safety. The research results provided a new perspective for the status assessment of lighting installations in immersed tunnels and also offered a theoretical basis for fine maintenance and repairs of lighting installations.https://www.frontiersin.org/articles/10.3389/fphy.2023.1159531/fullimmersed tunneldeep learningluminaire failuretraffic safetysafety assessment |
| spellingShingle | Meng Yang Meng Yang Shanfeng Lu Hao Ding Hao Ding Jianzhong Chen Jianzhong Chen Traffic safety assessment method of the immersed tunnel based on small target visual recognition image Frontiers in Physics immersed tunnel deep learning luminaire failure traffic safety safety assessment |
| title | Traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| title_full | Traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| title_fullStr | Traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| title_full_unstemmed | Traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| title_short | Traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| title_sort | traffic safety assessment method of the immersed tunnel based on small target visual recognition image |
| topic | immersed tunnel deep learning luminaire failure traffic safety safety assessment |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2023.1159531/full |
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