基于改进Faster R-CNN的纸病检测算法

目的 达到纸病检测中能够充分提取纸病特征、提高检测精度、降低小目标漏检率的目标。方法 基于Faster R-CNN的检测算法进行改进，主要改进的做法是利用深度残差网络ResNet-50替换原模型的骨干特征提取网络VGG16，以保留更多的纸病特征信息，增强特征网络对纸张缺陷的提取能力;在算法中添加空间和通道的双重注意力机制CBAM，用来提高纸病检测精度;将ROI-Pooling替换为ROI-Align，增强网络的泛化能力。结果 实验结果表明，改进后的算法平均精度达到98%，较原算法平均精度提升了9%。结论 改进后的算法能够充分提取纸病特征信息，有效提高了纸病的检测精度，以及提高了小目标纸病的检测率，降低了错漏检率。

Paper Defect Detection Algorithm Based on Improved Faster R-CNN

The work aims to achieve the goal of fully extracting paper defect features, improving detection accuracy and reducing detection rate of small targets in paper defect detection. The detection algorithm was improved based on Faster R-CNN. The main improvements were as follows:the backbone feature extraction network VGG16 of the original model was replaced by the deep residual network ResNet-50 to retain more feature information of paper defect and enhance the feature network''s ability to extract paper defects. The dual attention mechanism CBAM of space and channel was added to the algorithm to improve the accuracy of paper defect detection. ROI-Pooling was replaced with ROI-Align to enhance the generalization ability of network. The experimental results indicated that the average accuracy of the improved algorithm reached 98%, which was 9% higher than that of the original algorithm. The improved algorithm can fully extract the feature information, effectively improve the detection accuracy of paper defect, improve the detection rate of small target paper defect, and reduce the error and miss detection rate.