Surface defect detection method of carbon fiber prepreg based on machine vision

Aiming at low efficiency, high cost and poor real-time of artificial detection of surface defects of carbon fiber prepregs, an automatic detection method based on machine vision was proposed. Two high resolution line scanning cameras were used to collect images quickly and continuously in the carbon fiber production line, from which 1 000 images with defects were randomly selected. After that, the image enhancement algorithm based on the atmospheric light scattering model was used to pre-process the images to eliminate the interference of white resin. The YOLOv2 object detection network was refined with 19 convolution layers and 5 maximum pooling layers for improvement in detect detection. Finally, the pre-processed images were trained, image features were extracted, image objects were identified, and the trained network was verified. The experimental results show that the proposed method has high accuracy and robustness under complex industrial environment, the recognition success rate in this research is over 94%, and the detection time of each image is less than 0.1 s, meeting the requirements of precision and real-time in industrial production. Copyright No content may be reproduced or abridged without authorization.