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.
Aircraft rivet crack defect detection method based on YOLOv5
Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024) ; 2024 ; Anshan, China
Proc. SPIE ; 13256
12.09.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Runway Crack Detection Based on YOLOV5
IEEE | 2021
|Military Aircraft Detection Using YOLOv5
Springer Verlag | 2023
|Aircraft-Skin-Rivet Thermocouple
Online Contents | 1998
Real-Time Military Aircraft Detection using YOLOv5
IEEE | 2024
|