Research and application of mining AI video edge computing technology
Currently, mining AI video systems mainly rely on ground servers for analysis and processing, which leads to issues such as high overall response latency, multi-system linkage delays, and high network bandwidth utilization. To address these issues, a lightweight and edge-deployable mining AI video e...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2024-12-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18215 |
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| author | ZHANG Liya HAO Bonan MA Zheng YANG Zhifang |
| author_facet | ZHANG Liya HAO Bonan MA Zheng YANG Zhifang |
| author_sort | ZHANG Liya |
| collection | DOAJ |
| description | Currently, mining AI video systems mainly rely on ground servers for analysis and processing, which leads to issues such as high overall response latency, multi-system linkage delays, and high network bandwidth utilization. To address these issues, a lightweight and edge-deployable mining AI video edge computing system was designed. A lightweight software development kit (SDK) framework based on the register machine was proposed to decouple operators, improving the parallel computing ability of the algorithms and reducing the storage requirements of the SDK. Grouping design of YOLOv7 convolution operation was conducted, and the Focus backbone network was optimized using identity mapping to reduce computation amount and streamline the network structure. Additionally, the attention mechanism from Transformer was incorporated to improve detection performance. The mining AI video server was developed by integrating the domestic intelligent chips and 5G communication modules, enabling the deployment and calculation of mine edge nodes. The experimental results showed that the mining AI video edge computing system had excellent response. After deploying the registration machine SDK and optimizing the YOLOv7 model, the average inference latency was 28 ms, 52% and 44% lower than that of React Native+YOLOv7 and MobileNet, respectively. Under various load conditions, the response latency of the mining AI video server was significantly lower than the minimum requirements for mine operation. The field industrial test results showed that when the mining AI video server was connected to an 8-channel camera, the response latency was 51 ms, and the bandwidth was maintained at 45 Mbit/s. Compared to using a ground server, the response latency was reduced by 59%, and the bandwidth increased by 15%. This enabled real-time and on-site analysis and processing of underground video data, effectively reducing the data transmission delay and improving the response rate and processing efficiency of video analysis. |
| format | Article |
| id | doaj-art-ce045c09397f40d3b733fe12ea814678 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-ce045c09397f40d3b733fe12ea8146782025-01-23T02:17:44ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2024-12-015012859210.13272/j.issn.1671-251x.18215Research and application of mining AI video edge computing technologyZHANG LiyaHAO BonanMA ZhengYANG ZhifangCurrently, mining AI video systems mainly rely on ground servers for analysis and processing, which leads to issues such as high overall response latency, multi-system linkage delays, and high network bandwidth utilization. To address these issues, a lightweight and edge-deployable mining AI video edge computing system was designed. A lightweight software development kit (SDK) framework based on the register machine was proposed to decouple operators, improving the parallel computing ability of the algorithms and reducing the storage requirements of the SDK. Grouping design of YOLOv7 convolution operation was conducted, and the Focus backbone network was optimized using identity mapping to reduce computation amount and streamline the network structure. Additionally, the attention mechanism from Transformer was incorporated to improve detection performance. The mining AI video server was developed by integrating the domestic intelligent chips and 5G communication modules, enabling the deployment and calculation of mine edge nodes. The experimental results showed that the mining AI video edge computing system had excellent response. After deploying the registration machine SDK and optimizing the YOLOv7 model, the average inference latency was 28 ms, 52% and 44% lower than that of React Native+YOLOv7 and MobileNet, respectively. Under various load conditions, the response latency of the mining AI video server was significantly lower than the minimum requirements for mine operation. The field industrial test results showed that when the mining AI video server was connected to an 8-channel camera, the response latency was 51 ms, and the bandwidth was maintained at 45 Mbit/s. Compared to using a ground server, the response latency was reduced by 59%, and the bandwidth increased by 15%. This enabled real-time and on-site analysis and processing of underground video data, effectively reducing the data transmission delay and improving the response rate and processing efficiency of video analysis.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18215intelligent mineai video analysisedge computingregister machineyolov7lightweight design |
| spellingShingle | ZHANG Liya HAO Bonan MA Zheng YANG Zhifang Research and application of mining AI video edge computing technology Gong-kuang zidonghua intelligent mine ai video analysis edge computing register machine yolov7 lightweight design |
| title | Research and application of mining AI video edge computing technology |
| title_full | Research and application of mining AI video edge computing technology |
| title_fullStr | Research and application of mining AI video edge computing technology |
| title_full_unstemmed | Research and application of mining AI video edge computing technology |
| title_short | Research and application of mining AI video edge computing technology |
| title_sort | research and application of mining ai video edge computing technology |
| topic | intelligent mine ai video analysis edge computing register machine yolov7 lightweight design |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18215 |
| work_keys_str_mv | AT zhangliya researchandapplicationofminingaivideoedgecomputingtechnology AT haobonan researchandapplicationofminingaivideoedgecomputingtechnology AT mazheng researchandapplicationofminingaivideoedgecomputingtechnology AT yangzhifang researchandapplicationofminingaivideoedgecomputingtechnology |