关键词: 印刷缺陷检测, 深度学习, 孪生卷积神经网络, 特征金字塔, 变化检测

Abstract: In order to solve the problems of poor robustness of the print defects detection methods based on traditional image processing technology and the object detection methods based on deep learning are not completely suitable for the detection tasks of print defects, the comparison ideas in template matching method were combined with semantic features in deep learning, the Deep Comparison Network (CoNet) could be used for the detection tasks of print defects was proposed. Firstly, the Deep Comparison Module (DCM) based on twin structure was proposed to mine the semantic relationship between the detection image and the reference image through extracting and fusing the feature maps of them in the semantic space. Then, based on the feature pyramid structure composed of asymmetric double channels, the Multi-scale Change Detection Module (MsCDM) was proposed to locate and classify print defects. On the published dataset of print circuit board defects, the mean Average Precision (mAP) of CoNet is 99.1%, which is increased by 9.8 percentage points and 1.5 percentage points respectively compared with the template matching method and Faster Region-based Convolutional Neural Network (Faster R-CNN). Compared with the two baseline models Max-Pooling Group Pyramid Pooling (MP-GPP) and Change-Detection Single Shot Detector (CD-SSD), which also adopt the idea of change detection, the mAP of the proposed method is increased by 0.5 percentage points and 0.8 percentage points respectively. With the same experimental setting, the mAP of CoNet is 69.8% on a more complex dataset of Lijin defects, which is not only 12 percentage points and 5.3 percentage points higher than that of template matching method and Faster R-CNN, but also 3.5 percentage points and 2.4 percentage points higher than that of MP-GPP and CD-SSD respectively. Besides, when the resolution of input image is 640×640, the average time consumption of CoNet is 35.7ms, which can absolutely meet the real-time requirements of industrial detection tasks.

Key words: print defects detection, deep learning, Siamese convolutional neural network, feature pyramid, change detection