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Safety distance warning for forklift driving obstacles based on improved YOLOv12
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Cheng ZHOU1, Wenjie DAI2, Shuhao WAN1, Likai JU1
China Safety Science Journal | 2026, 36(3) : 89 - 97
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China Safety Science Journal | 2026, 36(3): 89-97
Safety Technology and Engineering
Safety distance warning for forklift driving obstacles based on improved YOLOv12
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Cheng ZHOU1, Wenjie DAI2, Shuhao WAN1, Likai JU1
Affiliations
  • 1Engineering Training Center, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China
  • 2Ningbo Weicheng Technology Co., Ltd., Ningbo Zhejiang 315000, China
Published: 2026-03-28 doi: 10.16265/j.cnki.issn1003-3033.2026.03.1262
Outline
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In order to solve the problems of high equipment price and large quantity demand in the existing forklift driving obstacle safety early warning distance measurement, a forklift driving obstacle safety distance early warning model based on image information was proposed. Firstly, based on deep learning technology, Squeeze-and-Excitation (SE) networks channel attention mechanism is introduced, and methods such as replacing the Intersection over Union(IoU) localization loss function with the Adaptive Threshold Focal Loss (ATFL) function are employed to improve the YOLOv12 algorithm for identifying obstacle targets in forklift travel. Secondly, on the basis of the improved YOLOv12 algorithm, the Kalman filter was introduced to improve the motion prediction model. And the distance detection method considering the camera pitch angle was used to accurately obtain the actual distance between different types of targets and the driving fork workshop. Thirdly, the kinematic process of forklift braking and forklift obstacle avoidance was analyzed, and the classification criteria of safe braking distance warning level and safety obstacle avoidance distance warning level were established, respectively. Finally, experiments were carried out to verify the feasibility of the safety warning distance of forklift driving obstacles based on image information. The results show that the real-time distance warning model can accurately identify obstacle targets in real-time and precisely determine the distance to obstacles within the permissible error range, enabling risk-level warning for obstacles during forklift operation.

YOLOv12  /  forklift operation  /  safety early warning distance  /  risk area  /  obstacle distance measurement  /  warning level
Cheng ZHOU, Wenjie DAI, Shuhao WAN, Likai JU. Safety distance warning for forklift driving obstacles based on improved YOLOv12[J]. China Safety Science Journal, 2026 , 36 (3) : 89 -97 . DOI: 10.16265/j.cnki.issn1003-3033.2026.03.1262
Year 2026 volume 36 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.03.1262
  • Receive Date:2025-09-30
  • Online Date:2026-07-08
  • Published:2026-03-28
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  • Received:2025-09-30
  • Revised:2025-12-10
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    1Engineering Training Center, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China
    2Ningbo Weicheng Technology Co., Ltd., Ningbo Zhejiang 315000, China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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