An Improved MaskRCNN Based Paper Disease Diagnosis Algorithm
This paper proposed a paper disease diagnosis algorithm based on an improved MaskRCNN network.Firstly,this algorithm im-proved 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 classifi-cation 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 algo-rithm.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.
paper disease diagnosisMaskRCNNVOVNetPrRoIPoolingSPANet
万方数据
维普
