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    State Awareness and Collision Risk Assessment Algorithm for Tower Crane Based on Bidirectional Inverse Perspective Mapping and Skeleton Key Points

    Source: Journal of Construction Engineering and Management:;2025:;Volume ( 151 ):;issue: 002::page 04024205-1
    Author:
    Fan Zhang
    ,
    Hongbo Liu
    ,
    Longxuan Wang
    ,
    Zhihua Chen
    ,
    Qian Zhang
    ,
    Liulu Guo
    DOI: 10.1061/JCEMD4.COENG-15723
    Publisher: American Society of Civil Engineers
    Abstract: To address the current issues of frequent tower crane accidents, imperfect supervision systems and the low adaptability of existing algorithms, a novel collision risk warning model for tower cranes is proposed. The model comprises a state-awareness module and a collision risk assessment module. The skeleton key points of the tower crane are proposed and constructed in the state-awareness module. On this basis, the You Only Look Once (YOLO) v8 algorithm detects the tower crane and its skeleton key points. The ByteTrack algorithm is used to track skeleton key points in real time. In the collision risk assessment module, the speed analysis method for the skeleton key points of the tower crane and the minimum safety distance assessment method between different tower cranes are proposed and compiled. Finally, the effectiveness and robustness of this collision risk warning model are verified by taking three practical projects as examples. The research results demonstrate that the precision of the box, the precision of key points, the mAP@0.5 of the box, and the mAP@0.5 of key points are 96.18%, 96.10%, 92.85%, and 92.53%, respectively. This paper’s method implements an intelligent assessment of tower crane collision risk. The model exhibits accurate visualization information in practical engineering applications and has broad application prospects.
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      State Awareness and Collision Risk Assessment Algorithm for Tower Crane Based on Bidirectional Inverse Perspective Mapping and Skeleton Key Points

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303954
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    • Journal of Construction Engineering and Management

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    contributor authorFan Zhang
    contributor authorHongbo Liu
    contributor authorLongxuan Wang
    contributor authorZhihua Chen
    contributor authorQian Zhang
    contributor authorLiulu Guo
    date accessioned2025-04-20T10:05:06Z
    date available2025-04-20T10:05:06Z
    date copyright12/11/2024 12:00:00 AM
    date issued2025
    identifier otherJCEMD4.COENG-15723.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303954
    description abstractTo address the current issues of frequent tower crane accidents, imperfect supervision systems and the low adaptability of existing algorithms, a novel collision risk warning model for tower cranes is proposed. The model comprises a state-awareness module and a collision risk assessment module. The skeleton key points of the tower crane are proposed and constructed in the state-awareness module. On this basis, the You Only Look Once (YOLO) v8 algorithm detects the tower crane and its skeleton key points. The ByteTrack algorithm is used to track skeleton key points in real time. In the collision risk assessment module, the speed analysis method for the skeleton key points of the tower crane and the minimum safety distance assessment method between different tower cranes are proposed and compiled. Finally, the effectiveness and robustness of this collision risk warning model are verified by taking three practical projects as examples. The research results demonstrate that the precision of the box, the precision of key points, the mAP@0.5 of the box, and the mAP@0.5 of key points are 96.18%, 96.10%, 92.85%, and 92.53%, respectively. This paper’s method implements an intelligent assessment of tower crane collision risk. The model exhibits accurate visualization information in practical engineering applications and has broad application prospects.
    publisherAmerican Society of Civil Engineers
    titleState Awareness and Collision Risk Assessment Algorithm for Tower Crane Based on Bidirectional Inverse Perspective Mapping and Skeleton Key Points
    typeJournal Article
    journal volume151
    journal issue2
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/JCEMD4.COENG-15723
    journal fristpage04024205-1
    journal lastpage04024205-16
    page16
    treeJournal of Construction Engineering and Management:;2025:;Volume ( 151 ):;issue: 002
    contenttypeFulltext
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