Aircraft rivet crack defect detection method based on YOLOv5

Minzheng Li; Tao Li

doi:10.1117/12.3037884

12 September 2024 Aircraft rivet crack defect detection method based on YOLOv5

Minzheng Li, Tao Li

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325626 (2024) https://doi.org/10.1117/12.3037884
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

The accurate detection of defects in aircraft rivets plays a crucial role in ensuring the safety of the aircraft. At present, the inspection of dense and diverse aircraft rivets defects mainly relies on manual completion, which seriously affects the efficiency of aircraft parts production. To solve this problem, the YOLOv5s-DMSA model is proposed in this paper. Based on YOLOv5s, it has made the following improvements: (1) The DMSA model is proposed in the backbone to increase the receptor field and facilitate multi-scale cross-channel extraction of more comprehensive feature information. (2) A tiny target detection head is added to the detection head, which is specially used to detect tiny targets such as rivets. The experimental results show that the mAP value of the proposed YOLOv5s-DMSA detection method is 7.7 percentage points higher than that of the standard YOLOv5s model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minzheng Li and Tao Li "Aircraft rivet crack defect detection method based on YOLOv5", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325626 (12 September 2024); https://doi.org/10.1117/12.3037884

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KEYWORDS

Defect detection

Target detection

Feature extraction

Data modeling

Head

Deep learning

Neck

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