Resumen
This paper proposes a lightweight detection model based on machine vision, YOLOv5-GC, to improve the efficiency and accuracy of detecting and classifying surface defects in preforming materials. During this process, clear images of the entire surface are difficult to obtain due to the stickiness, high reflectivity, and black resin of the thermosetting plain woven prepreg. To address this challenge, we built a machine vision platform equipped with a linescan camera and high-intensity linear light source that captures surface images of the material during the preforming process. To solve the problem of defect detection in the case of extremely small and imbalanced samples, we adopt a transfer learning approach based on the YOLOv5 neural network for defect recognition and introduce a coordinate attention and Ghost Bottleneck module to improve recognition accuracy and speed. Experimental results demonstrate that the proposed approach achieves rapid and high-precision identification of surface defects in preforming materials, outperforming other state-of-the-art methods. This work provides a promising solution for surface defect detection in preforming materials, contributing to the improvement of composite material quality.