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Engineering scene target detection method based on improved YOLOv8
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Zhangjie DING1, Jian HUANG2, 3, Linyuan XIE1, Yongming BIAN1
Chinese Journal of Construction Machinery | 2025, 23(2) : 366 - 370
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Chinese Journal of Construction Machinery | 2025, 23(2): 366-370
• Performance Mensuration, Experimentation and Fault Diagnosis •
Engineering scene target detection method based on improved YOLOv8
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Zhangjie DING1, Jian HUANG2, 3, Linyuan XIE1, Yongming BIAN1
Affiliations
  • 1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
  • 2. CCCC Second Harbor Engineering Company Ltd., Wuhan 430040, Hubei, China
  • 3. CCCC Highway Bridge National Engineering Research Centre Ltd., Beijing 100120, China
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With the wide application of target detection technology in unmanned construction and other scenarios, traditional target detection algorithms face challenges such as low recognition accuracy, large computational volume, and slow processing speed in complex engineering environments. Based on these challenges, this paper proposes a target detection method based on improved YOLOv8 for engineering scenes, which improves the C2f structure by introducing the star block in YOLOv8, significantly reducing the number of model parameters and computational volume while ensuring the detection accuracy. Based on this, this paper introduces a lightweight shared and detail-enhanced convolutional detection head, which further improves the detail-capturing ability of the detection head and significantly reduces the computational burden. The experimental results show that compared with YOLOv8n on Roboflow-based engineering scene dataset, the mAP@0.5 and mAP@0.5: 0.95 of the improved model improves by 0.3% and 2.0%, while the number of parameters and computational volume decreases by 36.7% and 34.6%, and improves the frames per second (FPS) by 23.3% accordingly, which verifies the superiority of the improved algorithm in terms of lightweight and detection accuracy.

target detection  /  YOLOv8  /  engineering scene  /  model lightweight
Zhangjie DING, Jian HUANG, Linyuan XIE, Yongming BIAN. Engineering scene target detection method based on improved YOLOv8[J]. Chinese Journal of Construction Machinery, 2025 , 23 (2) : 366 -370 .
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  • Online Date：2025-12-16
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    1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
    2. CCCC Second Harbor Engineering Company Ltd., Wuhan 430040, Hubei, China
    3. CCCC Highway Bridge National Engineering Research Centre Ltd., Beijing 100120, China
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