Detecting Algorithm based on the Improved YOLOv8s for a Weak Feature Defect of Aviation Clamps

Qinpeng Luo; Yinhua Liao

doi:10.62051/ijmee.v4n3.03

Authors

Qinpeng Luo
Yinhua Liao

DOI:

https://doi.org/10.62051/ijmee.v4n3.03

Keywords:

Aviation Clamps, Weak Feature Defect, YOLOv8 Algorithm, Defect Detection

Abstract

There are some weak defects on the surface of aviation clamps. Because they are very weak, it is difficult to identify them efficiently and accurately by the existing visual detecting algorithms, and the existing methods have high arithmetic power requirements for vision detecting systems. So, this work proposes a detecting algorithm for a weak feature defect detection of aviation clamps (YOLO-OGS). Firstly, in order to improve the ability of convolutional operations of extract features and decrease the model's GFLOPs, the Multidimensional dynamic convolutional ODConv is added to the backbone network of YOLOv8. Then, in order to reduce the complexity of the model while increasing the effectiveness of feature fusion at various levels by keeping more of the hidden connections in the channels, the GhostSlimFPN paradigm network structure, which contains GSConv convolution and slim-neck structure, is introduced in the neck network. Finally, the Shuffle Attention module is used to widen the image's sensory field and enhance the details of weak flaws. Based on the aviation clamp defect data set, the comparative analysis results of YOLO-OGS and YOLOv8s algorithms show that YOLO-OGS decreases the GFLOPs by 14.4% and increases precision, recall, mAP@0.5, and GFLOPs by 4%, 6.4%, and 3.4%, respectively. And compared with the other existing mainstream networks YOLOv6, YOLOv8s, YOLOv8n, YOLOv5n, YOLOv5s, YOLOv7, YOLOv3-tiny. 8.3%, 3.4%, 4.4%, 15.4%, 8%, 13.4%, and 12% improvement in mAP@0.5.

References

M. Ghobakhloo, "Industry 4.0, digitization, and opportunities for sustainability," Journal of cleaner production, vol. 252, pp. 119869, Apr.2020.

[2] G. Vial, "Understanding digital transformation: A review and a research agenda," Managing digital transformation, vol. 28, no. 3, pp. 13-66, Jun.2021.

[3] G. Li, C. Wang, D. Zhang, and G. Yang, "An improved feature selection method based on random forest algorithm for wind turbine condition monitoring," Sensors, vol. 21, no. 16, pp. 5654, Aug.2021.

[4] R. Shanmugamani, M. Sadique, and B. Ramamoorthy, "Detection and classification of surface defects of gun barrels using computer vision and machine learning," Measurement, vol. 60, pp. 222-230, Jan.2015.

[5] J.-K. Park, B.-K. Kwon, J.-H. Park, and D.-J. Kang, "Machine learning-based imaging system for surface defect inspection," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 3, pp. 303-310, Jul.2016.

[6] Z. Zhong and Y. Hu, "A fast oxidation region detection algorithm based on differential geometry approach for high-density flexible integrated circuit packaging substrates," Measurement Science and Technology, vol. 29, no. 10, pp. 105401, Aug.2018.

[7] C. Jian, J. Gao, and Y. Ao, "Automatic surface defect detection for mobile phone screen glass based on machine vision," Applied Soft Computing, vol. 52, pp. 348-358, Mar.2017.

[8] C. Xu, L. Li, J. Li, and C. Wen, "Surface defects detection and identification of lithium battery pole piece based on multi-feature fusion and PSO-SVM," Ieee Access, vol. 9, pp. 85232-85239, Mar.2021.

[9] J. Guo, Y. Yang, X. Xiong, Y. Yang, and M. Shao, "Brake disc positioning and defect detection method based on improved Canny operator," IET Image Processing, vol. 18, no. 5, pp. 1283-1295, Apr.2024.

[10] Y. Xu, K. Zhang, and L. Wang, "Metal surface defect detection using modified YOLO," Algorithms, vol. 14, no. 9, pp. 257, Aug.2021.

[11] Z. Xu, Z. Wu, and W. Fan, "Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence," Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 235, no. 5, pp. 761-768, Feb,2021.

[12] Y. J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk, "Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types," Computer‐Aided Civil and Infrastructure Engineering, vol. 33, no. 9, pp. 731-747, Sept.2018.

[13] X. Ye, J. Ye, Z. He, D. Zhang, X. Hu, and Q. Chen, "A Novel Defect Detection Method for Ferrite Shield Surface Defects by Improved Faster R-CNN," Scientific Programming, vol. 2022, Nov.2022.

[14] X. Wang, H. Gao, Z. Jia, and Z. Li, "BL-YOLOv8: An improved road defect detection model based on YOLOv8," Sensors, vol. 23, no. 20, pp. 8361, Oct.2023.

[15] Y. Zhang, S. Shen, and S. Xu, "Strip steel surface defect detection based on lightweight YOLOv5," Frontiers in Neurorobotics, vol. 17, pp. 1263739, Oct.2023.

[16] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Jul.2023, pp. 7464-7475.

[17] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, Apr.2020.

[18] R. Sunkara and T. Luo, "No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Aug.2022: Springer, pp. 443-459.

[19] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "Scaled-yolov4: Scaling cross stage partial network," in Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, Nov.2021, pp. 13029-13038.

[20] X. Wang, R. Girshick, A. Gupta, and K. He, "Non-local neural networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun.2018, pp. 7794-7803.

[21] Y. Chen, X. Dai, M. Liu, D. Chen, L. Yuan, and Z. Liu, "Dynamic convolution: Attention over convolution kernels," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Mar.2020, pp. 11030-11039.

[22] B. Yang, G. Bender, Q. V. Le, and J. Ngiam, "Condconv: Conditionally parameterized convolutions for efficient inference," Advances in neural information processing systems, vol. 32, Apr.2019.

[23] C. Li, A. Zhou, and A. Yao, "Omni-dimensional dynamic convolution," arXiv preprint arXiv:2209.07947, Sept.2022.

[24] X. Lin, L. Ma, W. Liu, and S.-F. Chang, "Context-gated convolution," in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16, 2020: Springer, pp. 701-718.

[25] H. Li, J. Li, H. Wei, Z. Liu, Z. Zhan, and Q. Ren, "Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles," arXiv preprint arXiv:2206.02424, Aug.2022.

[26] J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun.2018, pp. 7132-7141.

[27] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "Cbam: Convolutional block attention module," in Proceedings of the European conference on computer vision (ECCV), Jul.2018, pp. 3-19.

[28] Q. Hou, D. Zhou, and J. Feng, "Coordinate attention for efficient mobile network design," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Mar.2021, pp. 13713-13722.

[29] T. Yu, X. Li, Y. Cai, M. Sun, and P. Li, "S^2-MLPv2: improved spatial-shift MLP architecture for vision," arXiv preprint arXiv:2108.01072, Aug.2021.

[30] Y. Yuan, L. Huang, J. Guo, C. Zhang, X. Chen, and J. Wang, "Ocnet: Object context network for scene parsing," arXiv preprint arXiv:1809.00916, Sept.2018.

[31] Q.-L. Zhang and Y.-B. Yang, "Sa-net: Shuffle attention for deep convolutional neural networks," in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun.2021: IEEE, pp. 2235-2239.

[32] M. Lin, Q. Chen, and S. Yan, "Network in network," arXiv preprint arXiv:1312.4400, Dec.2013.

[33] Q. Luo, X. Fang, J. Su, J. Zhou, B. Zhou, C. Yang et al., "Automated visual defect classification for flat steel surface: a survey," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9329-9349, Dec.2020.