Lightweight detection method for weld defects of metro train based on improved YOLOv8
To address the issues of the current metro train body welding quality inspection technology,such as the large size of the detection model and low detection accuracy and efficiency,a lightweight detection method of weld defects based on improved YOLOv8 was proposed.Firstly,images of internal defects in butt welds were collected by using a phased array ultrasonic detector,and a welding defect dataset was created through image preprocessing.Then,based on the YOLOv8 model,the original loss function is optimized using Inner-SIoU,the C2f module was replaced with C2f-PConv,and the LSKA module and SE attention mechanism were introduced to establish a defect quality detection model for metro train body welding seams based on improved YOLOv8.The proposed model could enhance the capability of extracting features from weld seam defects and facilitating multi-scale feature fusion.Finally,the improved YOLOv8 model was trained and tested on the weld defect data set.Experimental results show that the size of the improved YOLOv8 model was 7.91 M,the detection accuracy of weld defects reaches 98.30％,and the detection speed reaches 138.9 frames per selend.Compared with the original YOLOv8 model,the model is smaller and has higher detection accuracy.
metro trainweld defect detectionYOLOv8lightweightphased array ultrasonic testing
NETL
NSTL
万方数据
