Defect detection of pultrusion plate based on improved YOLOv5s

doi:10.19936/j.cnki.2096-8000.20250528.017

Abstract

Abstract: In order to solve the problems of low detection precision and slow detection speed in traditional pultrusion plate defect detection methods, a defect data set of glass fiber pultrusion plate was created, and a defect detection model of glass fiber pultrusion plate based on improved YOLOv5s was proposed. The main improvements were as follows: in the feature extraction network part, EvcBlock module was added to enhance the feature extraction ability of small targets, and CBAM attention mechanism was added to improve the attention of important features; the lightweight model was realized by using C3-FASTER module to optimize the C3 module; a new type of loss function ShapeIoU with shape loss was introduced into the detection end, which optimized the fitting effect of the predicted frame and the real frame, and improved the precision of defect detection. The experimental results demonstrate that: in comparison to the original YOLOv5s model, the mAP@0.5 of the improved YOLOv5s model has increased by 3.6%, reaching 88.7%, while the number of parameters has been reduced by 2.1%. The detection speed of the improved model is 121.95 frames per second, and its overall performance is superior to 5 models such as YOLOv8s, which can meet the needs of pultrusion plate defect detection.

Key words: pultrusion plate, YOLOv5s, defect detection, EvcBlock, C3-faster, ShapeIoU, composites

CLC Number:

TB332

XU Dongliang, LAI Jiuheng, YANG Huilan. Defect detection of pultrusion plate based on improved YOLOv5s[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(5): 132-141.

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