Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine
In the driverless transport and operation system of surface mine, the roadside perception system is used to assist driverless vehicles by providing road condition information. The driverless system currently applied in mine trucks realizes roadside perception based on the multi-sensor fusion technol...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2023-06-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2023.03.012 |
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| _version_ | 1849224923250163712 |
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| author | YUE Wei LIN Jun KANG Gaoqiang YOU Jun XU Yanghan TONG Hao |
| author_facet | YUE Wei LIN Jun KANG Gaoqiang YOU Jun XU Yanghan TONG Hao |
| author_sort | YUE Wei |
| collection | DOAJ |
| description | In the driverless transport and operation system of surface mine, the roadside perception system is used to assist driverless vehicles by providing road condition information. The driverless system currently applied in mine trucks realizes roadside perception based on the multi-sensor fusion technology, consisting of cameras, laser radars and millimeter wave radars. However, this system has several drawbacks, such as high system cost, a complicated structure and poor robustness. In this regard, this paper proposes a roadside perception approach based on an improved DeepSORT algorithm. This approach involves using cameras to acquire image data on vehicles and pedestrians in the mine, which are accurately identified by the YOLOv5s algorithm. Then, the improved DeepSORT algorithm tracks the identified objects in real-time, enabling statistical analysis to provide various functions, including vehicle traffic statistics, abnormal parking detection and pedestrian intrusion detection. The proposed approach was tested at the No. 8 intersection of Xiwan Surface Mine of Shaanxi Shenyan Coal Co., Ltd. The results show that using a single sensor approach can effectively achieve the recognition and tracking of vehicles and pedestrians at mine intersection, reduce the complexity of the roadside perception system and save costs compared to the roadside perception technology based on multi-sensor fusion. |
| format | Article |
| id | doaj-art-04db769eef004dcd844b3874744095f4 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-04db769eef004dcd844b3874744095f42025-08-25T06:48:28ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272023-06-01899439335000Application of Roadside Perception Method Based on Improved DeepSORT in Surface MineYUE WeiLIN JunKANG GaoqiangYOU JunXU YanghanTONG HaoIn the driverless transport and operation system of surface mine, the roadside perception system is used to assist driverless vehicles by providing road condition information. The driverless system currently applied in mine trucks realizes roadside perception based on the multi-sensor fusion technology, consisting of cameras, laser radars and millimeter wave radars. However, this system has several drawbacks, such as high system cost, a complicated structure and poor robustness. In this regard, this paper proposes a roadside perception approach based on an improved DeepSORT algorithm. This approach involves using cameras to acquire image data on vehicles and pedestrians in the mine, which are accurately identified by the YOLOv5s algorithm. Then, the improved DeepSORT algorithm tracks the identified objects in real-time, enabling statistical analysis to provide various functions, including vehicle traffic statistics, abnormal parking detection and pedestrian intrusion detection. The proposed approach was tested at the No. 8 intersection of Xiwan Surface Mine of Shaanxi Shenyan Coal Co., Ltd. The results show that using a single sensor approach can effectively achieve the recognition and tracking of vehicles and pedestrians at mine intersection, reduce the complexity of the roadside perception system and save costs compared to the roadside perception technology based on multi-sensor fusion.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2023.03.012driverless systemroadside perceptionedge computingdeep learningsingle sensorobject detectionobject trackingsurface mine |
| spellingShingle | YUE Wei LIN Jun KANG Gaoqiang YOU Jun XU Yanghan TONG Hao Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine Kongzhi Yu Xinxi Jishu driverless system roadside perception edge computing deep learning single sensor object detection object tracking surface mine |
| title | Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine |
| title_full | Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine |
| title_fullStr | Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine |
| title_full_unstemmed | Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine |
| title_short | Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine |
| title_sort | application of roadside perception method based on improved deepsort in surface mine |
| topic | driverless system roadside perception edge computing deep learning single sensor object detection object tracking surface mine |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2023.03.012 |
| work_keys_str_mv | AT yuewei applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine AT linjun applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine AT kanggaoqiang applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine AT youjun applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine AT xuyanghan applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine AT tonghao applicationofroadsideperceptionmethodbasedonimproveddeepsortinsurfacemine |