Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation
Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9612480 |
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| _version_ | 1832557130501062656 |
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| author | Qingfeng Huang Yage Huang Zhiwei Zhang Yujie Zhang Weijian Mi Chao Mi |
| author_facet | Qingfeng Huang Yage Huang Zhiwei Zhang Yujie Zhang Weijian Mi Chao Mi |
| author_sort | Qingfeng Huang |
| collection | DOAJ |
| description | Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents. |
| format | Article |
| id | doaj-art-cf46149a734541afb74c5c31582c057f |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-cf46149a734541afb74c5c31582c057f2025-02-03T05:43:39ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/9612480Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting OperationQingfeng Huang0Yage Huang1Zhiwei Zhang2Yujie Zhang3Weijian Mi4Chao Mi5Container Supply Chain Technology Engineering Research Center Ministry of EducationLogistic Engineering SchoolShanghai SMUVision Smart Technology Ltd.Logistic Engineering SchoolContainer Supply Chain Technology Engineering Research Center Ministry of EducationContainer Supply Chain Technology Engineering Research Center Ministry of EducationTruck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.http://dx.doi.org/10.1155/2021/9612480 |
| spellingShingle | Qingfeng Huang Yage Huang Zhiwei Zhang Yujie Zhang Weijian Mi Chao Mi Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation Journal of Advanced Transportation |
| title | Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation |
| title_full | Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation |
| title_fullStr | Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation |
| title_full_unstemmed | Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation |
| title_short | Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation |
| title_sort | truck lifting prevention system based on vision tracking for container lifting operation |
| url | http://dx.doi.org/10.1155/2021/9612480 |
| work_keys_str_mv | AT qingfenghuang truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation AT yagehuang truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation AT zhiweizhang truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation AT yujiezhang truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation AT weijianmi truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation AT chaomi truckliftingpreventionsystembasedonvisiontrackingforcontainerliftingoperation |