基于机器视觉的碳纤维预浸料表面缺陷检测方法

针对碳纤维预浸料表面缺陷人工检测方法效率低、成本高、实时性差等问题,提出基于机器视觉的碳纤维预浸料表面缺陷自动检测方法。首先,在碳纤维预浸料生产线上,采用2台高分辨率线扫描相机快速连续采集图像,从中随机选择带有缺陷的图像1 000张;其次,基于大气光散射模型对图像进行去雾增强处理,以消除白色树脂的干扰;然后,改进具有19个卷积层和5个最大值池化层的YOLOv2目标检测算法,用于缺陷的检测;最后,对预处理后的图像进行网络训练提取图像特征,识别图像目标,并对训练好的网络进行实验验证。结果表明:该方法在复杂的工业环境下,具有较高的识别精度和鲁棒性,识别成功率达到94%以上,且每张图像的检测时间不超过 0.1 s,可满足工业生产中精度和实时性要求。

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.