Weld defect detection of aviation aluminum alloy based on improved YOLOv8
In order to improve the efficiency and accuracy of automatic detection,an improved YOLOv8 detection method was proposed.Retinex image enhancement algorithm combing guided filtering was used to improve the contrast of digital radiograph images.Then,the digital radiography images was rotated and flipped to extend the data-set.In the process of model improvement,the Bottleneck module in C2f was replaced by GhostBottleneck module to reduce additional redundant parameters,so the lightweight model was acquired.In addition,spatial attention mechanism was introduced to obtain more spatial information of the defect.The regression range of the prediction box was adjusted to improve the detection accuracy of the proposed model.Several common aluminum alloy weld defects were used for experimental testing and verification.It was verified that the mAP of the improved YOLOv8 was 92.9％,which was better than Faster-RCNN,SSD and YOLOv8.The proposed model can be used for detecting the weld defect.
digital radiographyimage enhancementautomatic identificationYOLOv8 algorithmweld defect
NETL
NSTL
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