【摘要】 本研究提出了一种基于改进MaskRCNN网络的纸病诊断算法。该算法首先在原有的MaskRCNN网络的基础上，使用轻量化头部骨干网络VOVNet和精细化的RoIPooling (PrRoIPooling)对原网络模型进行改进，以减少原网络模型的参数使用量，提升检测分类速度；其次添加空间金字塔注意力机制（SPANet），以解决原网络模型对于小目标检测精确度不高的问题。采集4 000多张纸病图像对本研究提出的算法进行仿真验证。结果表明，改进的MaskRCNN模型比原网络模型在平均精度上提升了3个百分点，速度上提升了15%，能够满足纸病诊断的实时性和准确性的实际需求。更多还原

【Abstract】 This paper proposed a paper disease diagnosis algorithm based on an improved MaskRCNN network. Firstly, this algorithm improved the network model by using a lightweight head backbone network VOVNet and a Precise RoIPooling(PrRoIPooling) on the basis of the original MaskRCNN network, in order to reduce the parameter usage of the original network model and improve the detection and classification speed. Secondly, a spatial pyramid attention mechanism(SPANet) was added to address the issue of low accuracy in detecting small objects in the original network model. More than 4 000 paper disease images were collected for simulation verification of the proposed algorithm. The results showed that the improved MaskRCNN model had increased average accuracy by 3 percentage points and speed by 15%compared to the original network model, which could meet the practical requirements of real-time and accuracy in paper disease diagnosis.更多还原