Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16
Abstract Ecological restoration of high and steep slopes (HSSs) in open pit mines (OPMs) is a key aspect of mining environmental management. However, relevant mine restoration image datasets are usually limited in size. Moreover, such images are susceptible to factors such as ambient light and occlu...
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Springer
2025-08-01
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| Online Access: | https://doi.org/10.1007/s42452-025-07634-6 |
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| author | Yongfeng Gong Jianwei Zhou Gang Zhang Ran Li Guorui Wang Xiaofeng He Zhiyong Hu |
| author_facet | Yongfeng Gong Jianwei Zhou Gang Zhang Ran Li Guorui Wang Xiaofeng He Zhiyong Hu |
| author_sort | Yongfeng Gong |
| collection | DOAJ |
| description | Abstract Ecological restoration of high and steep slopes (HSSs) in open pit mines (OPMs) is a key aspect of mining environmental management. However, relevant mine restoration image datasets are usually limited in size. Moreover, such images are susceptible to factors such as ambient light and occlusion, leading to inefficiency and subjectivity in traditional monitoring methods. To improve the accuracy and automation of slope restoration monitoring, an image recognition system combining improved visual geometry group 16-layer network (IVGG16), U-shaped network (U-Net), and capsule networks (CapsNet) is designed in this study. Meanwhile, in the baseline model U-Net, IVGG16 and CapsNet are introduced to build a semantic segmentation model to improve image recognition accuracy. The experimental results indicated that the mean intersection over union (mIoU) of only introducing CapsNet was 87.45%, which was improved by 2.50% compared with the baseline U-Net model. The mIoU of introducing only IVGG16 was then improved by 4.79–89.74%. Improving the VGG16 model enhanced feature extraction capability and effectively reduced overfitting. Whereas, CapsNet could capture the spatial hierarchical relationships, enhance the detail sensitivity, and optimize the spatial relationship modeling. The present model demonstrated excellent robustness under challenging conditions such as complex lighting and seasonal changes. For example, under rainy conditions, the fluctuation range of its mIoU could be controlled within 6.45%, and the model maintained a stable output, with a significantly better performance than traditional methods. In the complex scenario of mine slope rehabilitation, the model mIoU proposed in this study was 90.16% and F1 Score was 94.86%. It had good segmentation accuracy and robustness, and the performance was optimized. This provides reliable technical support for intelligent monitoring of ecological restoration and promotes the green and sustainable development of mining engineering. |
| format | Article |
| id | doaj-art-d0f5e439a9434e6180ce85fd1b97a198 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-d0f5e439a9434e6180ce85fd1b97a1982025-08-20T03:43:10ZengSpringerDiscover Applied Sciences3004-92612025-08-017911710.1007/s42452-025-07634-6Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16Yongfeng Gong0Jianwei Zhou1Gang Zhang2Ran Li3Guorui Wang4Xiaofeng He5Zhiyong Hu6Ningxia Hui Autonomous Region Land Resources Survey and Monitoring InstituteSchool of Environmental Studies, China University of Geosciences (Wuhan)Ningxia Hui Autonomous Region Land Resources Survey and Monitoring InstituteSchool of Environmental Studies, China University of Geosciences (Wuhan)Ningxia Hui Autonomous Region Land Resources Survey and Monitoring InstituteNingxia Hui Autonomous Region Land Resources Survey and Monitoring InstituteNingxia Hui Autonomous Region Land Resources Survey and Monitoring InstituteAbstract Ecological restoration of high and steep slopes (HSSs) in open pit mines (OPMs) is a key aspect of mining environmental management. However, relevant mine restoration image datasets are usually limited in size. Moreover, such images are susceptible to factors such as ambient light and occlusion, leading to inefficiency and subjectivity in traditional monitoring methods. To improve the accuracy and automation of slope restoration monitoring, an image recognition system combining improved visual geometry group 16-layer network (IVGG16), U-shaped network (U-Net), and capsule networks (CapsNet) is designed in this study. Meanwhile, in the baseline model U-Net, IVGG16 and CapsNet are introduced to build a semantic segmentation model to improve image recognition accuracy. The experimental results indicated that the mean intersection over union (mIoU) of only introducing CapsNet was 87.45%, which was improved by 2.50% compared with the baseline U-Net model. The mIoU of introducing only IVGG16 was then improved by 4.79–89.74%. Improving the VGG16 model enhanced feature extraction capability and effectively reduced overfitting. Whereas, CapsNet could capture the spatial hierarchical relationships, enhance the detail sensitivity, and optimize the spatial relationship modeling. The present model demonstrated excellent robustness under challenging conditions such as complex lighting and seasonal changes. For example, under rainy conditions, the fluctuation range of its mIoU could be controlled within 6.45%, and the model maintained a stable output, with a significantly better performance than traditional methods. In the complex scenario of mine slope rehabilitation, the model mIoU proposed in this study was 90.16% and F1 Score was 94.86%. It had good segmentation accuracy and robustness, and the performance was optimized. This provides reliable technical support for intelligent monitoring of ecological restoration and promotes the green and sustainable development of mining engineering.https://doi.org/10.1007/s42452-025-07634-6MinesEcological restorationImage recognitionVGG16U-NetCapsNet |
| spellingShingle | Yongfeng Gong Jianwei Zhou Gang Zhang Ran Li Guorui Wang Xiaofeng He Zhiyong Hu Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 Discover Applied Sciences Mines Ecological restoration Image recognition VGG16 U-Net CapsNet |
| title | Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 |
| title_full | Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 |
| title_fullStr | Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 |
| title_full_unstemmed | Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 |
| title_short | Image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved VGG16 |
| title_sort | image recognition system for ecological restoration of high and steep slopes in open pit mines based on improved vgg16 |
| topic | Mines Ecological restoration Image recognition VGG16 U-Net CapsNet |
| url | https://doi.org/10.1007/s42452-025-07634-6 |
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