Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9895 |
| _version_ | 1797582818661892096 |
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| author | Tingbo Xie Xifan Yao |
| author_facet | Tingbo Xie Xifan Yao |
| author_sort | Tingbo Xie |
| collection | DOAJ |
| description | The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm’s robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses. |
| first_indexed | 2024-03-10T23:27:57Z |
| format | Article |
| id | doaj.art-b170c6fd0d604933ba0a745c01bc0031 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:27:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b170c6fd0d604933ba0a745c01bc00312023-11-19T07:53:12ZengMDPI AGApplied Sciences2076-34172023-09-011317989510.3390/app13179895Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORTTingbo Xie0Xifan Yao1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, ChinaThe future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm’s robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses.https://www.mdpi.com/2076-3417/13/17/9895deep learningYOLOv5DeepSORTlogistics warehouse |
| spellingShingle | Tingbo Xie Xifan Yao Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT Applied Sciences deep learning YOLOv5 DeepSORT logistics warehouse |
| title | Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT |
| title_full | Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT |
| title_fullStr | Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT |
| title_full_unstemmed | Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT |
| title_short | Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT |
| title_sort | smart logistics warehouse moving object tracking based on yolov5 and deepsort |
| topic | deep learning YOLOv5 DeepSORT logistics warehouse |
| url | https://www.mdpi.com/2076-3417/13/17/9895 |
| work_keys_str_mv | AT tingboxie smartlogisticswarehousemovingobjecttrackingbasedonyolov5anddeepsort AT xifanyao smartlogisticswarehousemovingobjecttrackingbasedonyolov5anddeepsort |