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Aircraft rivet and detachment anomaly detection algorithm based on improved YOLOv8n
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Zhenghong XIA1, Hu HE1, Lei YANG1, Jianjun WU2, Lu LIU3
China Safety Science Journal | 2025, 35(2) : 66 - 72
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China Safety Science Journal | 2025, 35(2): 66-72
Safety engineering technology
Aircraft rivet and detachment anomaly detection algorithm based on improved YOLOv8n
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Zhenghong XIA1, Hu HE1, Lei YANG1, Jianjun WU2, Lu LIU3
Affiliations
  • 1 School of Air Traffic Management,Civil Aviation Flight University of China,Guanghan Sichuan 618307,China
  • 2 North Regional Administration,Civil Aviation Administration of China,Beijing 100621,China
  • 3 Chengdu Aircraft Design and Research Institute,Aviation Industry Corporation of China,Ltd.,Chengdu Sichuan 610091,China
Published: 2025-02-28 doi: 10.16265/j.cnki.issn1003-3033.2025.02.0880
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In order to address the issue of missed detections during small target detection of aircraft rivets,an improved YOLOv8n algorithm for the detection of aircraft rivets and their anomalies was proposed. First,by adding a small object detection head,the shallow detail information in the backbone network was better fused,enhancing the model's feature fusion capability and its ability to recognize and locate small rivet targets. Second,the first two convolutions in the backbone network were replaced with SPD-Conv,which reduces information loss during down sampling through the combination of feature map reorganization and non-stride convolutions. Finally,large separable kernel attention (LSKA) was integrated into the spatial pyramid pooling fast (SPPF) module,capturing the dependencies between spatial and channel dimensions by calculating spatial and channel weights on each feature map and adjusting the feature maps to enhance the algorithm's ability to extract and recognize rivet feature information. Ablation experiments and comparative experiments were conducted based on a self-built aircraft rivet dataset. The results show that the proposed algorithm can achieve real-time identification of aircraft rivets and their anomalies,with precision,recall,and mean average precision (mAP) values improved by 6.5%,16%,and 15%,respectively,compared to the YOLOv8n algorithm. The detection performance is also significantly better than other mainstream algorithms.

improved YOLOv8n  /  aircraft rivets  /  detachment  /  anomaly detection  /  space to depth convolution(SPD-Conv)  /  ablation experiment
Zhenghong XIA, Hu HE, Lei YANG, Jianjun WU, Lu LIU. Aircraft rivet and detachment anomaly detection algorithm based on improved YOLOv8n[J]. China Safety Science Journal, 2025 , 35 (2) : 66 -72 . DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0880
Year 2025 volume 35 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.02.0880
  • Receive Date:2024-09-25
  • Online Date:2025-07-05
  • Published:2025-02-28
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History
  • Received:2024-09-25
  • Revised:2024-11-25
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Affiliations
    1 School of Air Traffic Management,Civil Aviation Flight University of China,Guanghan Sichuan 618307,China
    2 North Regional Administration,Civil Aviation Administration of China,Beijing 100621,China
    3 Chengdu Aircraft Design and Research Institute,Aviation Industry Corporation of China,Ltd.,Chengdu Sichuan 610091,China
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表12种不同金属材料的力学参数

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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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