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Detection algorithm of aircraft skin defects based on improved YOLOv8n

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Abstract

In order to solve the problem of small targets being prone to false detection and missed detection in aircraft skin defect detection under complex backgrounds, the model of aircraft skin defect detection based on improved YOLOv8n is proposed in this paper. Firstly, the Shuffle Attention +  + module is incorporated into the network, combined with the residual connection idea, to more efficiently fuse feature map information; Secondly, SIOU and Focal Loss are used to replace CIOU as the regression loss functions to balance positive and negative samples in complex backgrounds and accelerate model convergence; Subsequently, the bidirectional feature pyramid network is used to modify the detection head and enhance multi-scale feature fusion. Furthermore, the depth-wise convolution module is used to replace the convolution module (Conv) in the neck part, which serves to reduce the parameters of the model and speed up the detection speed. Finally, an aircraft skin defect dataset is established, combined with Mosaic data enhancement to prevent the model from overfitting, and adopted the class balancing strategy to avoid class bias. The experimental results show that the detection accuracy of our improved YOLOv8n model is 97.9%, which is 7.3% higher than the baseline model. The model’s recall rate, the mean average precision, and F1 scores are improved by 13.9%, 6.6%, and 11.0%, respectively. The detection speed has achieved 139FPS, fulfilling the requirements of high accuracy and real-time performance in small target aircraft skin defect detection tasks.

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Funding

This study was supported by the National Natural Science Foundation of China (grant no. U2133202), and Major Science and Technology Special Projects in Sichuan Province, China (grant no. 2021ZDZX0001).

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Authors and Affiliations

  1. Engineering Techniques Training Center, Civil Aviation University of China, Tianjin, 300300, China

    Hao Wang

  2. College of Aeronautics Engineering, Civil Aviation University of China, Tianjin, 300300, China

    Lanxue Fu

  3. Institute of Science and Technology Innovation, Civil Aviation University of China, Tianjin, 300300, China

    Liwen Wang

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  1. Hao Wang
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  2. Lanxue Fu
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  3. Liwen Wang
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Contributions

Lanxue Fu proposed this method and trained the model, as well as collected data to establish the dataset. Hao Wang directed the writing of the thesis and helped revise and polish it.

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Correspondence to Hao Wang.

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Wang, H., Fu, L. & Wang, L. Detection algorithm of aircraft skin defects based on improved YOLOv8n. SIViP 18, 3877–3891 (2024). https://doi.org/10.1007/s11760-024-03049-9

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  • DOI: https://doi.org/10.1007/s11760-024-03049-9

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