Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots
In recent years, the global cold chain logistics market has grown rapidly, but the level of automation remains low. Compared to traditional logistics, automation in cold storage logistics requires a balance between safety and efficiency, and the current detection algorithms are poor at meeting these...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/12/8/334 |
| _version_ | 1827730756886069248 |
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| author | Zejiong Wei Feng Tian Zhehang Qiu Zhechen Yang Runyang Zhan Jianming Zhan |
| author_facet | Zejiong Wei Feng Tian Zhehang Qiu Zhechen Yang Runyang Zhan Jianming Zhan |
| author_sort | Zejiong Wei |
| collection | DOAJ |
| description | In recent years, the global cold chain logistics market has grown rapidly, but the level of automation remains low. Compared to traditional logistics, automation in cold storage logistics requires a balance between safety and efficiency, and the current detection algorithms are poor at meeting these requirements. Therefore, based on YOLOv5, this paper proposes a recognition and grasping system for cartons in cold storage warehouses. A human–machine interaction system is designed for the cold storage environment, enabling remote control and unmanned grasping. At the algorithm level, the CA attention mechanism is introduced to improve accuracy. The Ghost lightweight module replaces the CBS structure to enhance runtime speed. The Alpha-DIoU loss function is utilized to improve detection accuracy. With the comprehensive improvements, the modified algorithm in this study achieves a 0.711% increase in mAP and a 0.7% increase in FPS while maintaining accuracy. Experimental results demonstrate that the CA attention mechanism increases fidelity by 2.32%, the Ghost lightweight module reduces response time by 13.89%, and the Alpha-DIoU loss function enhances positioning accuracy by 7.14%. By incorporating all the improvements, the system exhibits a 2.16% reduction in response time, a 4.67% improvement in positioning accuracy, and a significant overall performance enhancement. |
| first_indexed | 2024-03-11T00:13:09Z |
| format | Article |
| id | doaj.art-11f6fd711009450981beebbc2c5fcf5c |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-11T00:13:09Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-11f6fd711009450981beebbc2c5fcf5c2023-11-18T23:49:08ZengMDPI AGActuators2076-08252023-08-0112833410.3390/act12080334Research on Machine Vision-Based Control System for Cold Storage Warehouse RobotsZejiong Wei0Feng Tian1Zhehang Qiu2Zhechen Yang3Runyang Zhan4Jianming Zhan5School of Mechatronics and Energy Engineering, NingboTech University, Ningbo 315100, ChinaNingbo Ruyi Joint Stock Co., Ltd., Ningbo 315100, ChinaSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310023, ChinaSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310023, ChinaSchool of Mechatronics and Energy Engineering, NingboTech University, Ningbo 315100, ChinaSchool of Mechatronics and Energy Engineering, NingboTech University, Ningbo 315100, ChinaIn recent years, the global cold chain logistics market has grown rapidly, but the level of automation remains low. Compared to traditional logistics, automation in cold storage logistics requires a balance between safety and efficiency, and the current detection algorithms are poor at meeting these requirements. Therefore, based on YOLOv5, this paper proposes a recognition and grasping system for cartons in cold storage warehouses. A human–machine interaction system is designed for the cold storage environment, enabling remote control and unmanned grasping. At the algorithm level, the CA attention mechanism is introduced to improve accuracy. The Ghost lightweight module replaces the CBS structure to enhance runtime speed. The Alpha-DIoU loss function is utilized to improve detection accuracy. With the comprehensive improvements, the modified algorithm in this study achieves a 0.711% increase in mAP and a 0.7% increase in FPS while maintaining accuracy. Experimental results demonstrate that the CA attention mechanism increases fidelity by 2.32%, the Ghost lightweight module reduces response time by 13.89%, and the Alpha-DIoU loss function enhances positioning accuracy by 7.14%. By incorporating all the improvements, the system exhibits a 2.16% reduction in response time, a 4.67% improvement in positioning accuracy, and a significant overall performance enhancement.https://www.mdpi.com/2076-0825/12/8/334cold chain warehousinghuman–machine interactionmachine visionobject detectionYOLOv5 |
| spellingShingle | Zejiong Wei Feng Tian Zhehang Qiu Zhechen Yang Runyang Zhan Jianming Zhan Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots Actuators cold chain warehousing human–machine interaction machine vision object detection YOLOv5 |
| title | Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots |
| title_full | Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots |
| title_fullStr | Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots |
| title_full_unstemmed | Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots |
| title_short | Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots |
| title_sort | research on machine vision based control system for cold storage warehouse robots |
| topic | cold chain warehousing human–machine interaction machine vision object detection YOLOv5 |
| url | https://www.mdpi.com/2076-0825/12/8/334 |
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