Patent ID: 11881015
Assignee: SHANGHAI JIAO TONG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. The method according to claim 1, wherein the method of building the Mask RCNN objection recognition network model comprising,
superimposing a plurality of residual network ResNet, y=F(x)+x;
establishing a region generative network, Pi=FC2[FC1[Pooling(f,Ri)]], and setting a threshold of 0.5, keeping a candidate region if Pi exceeds 0.5, and discarding the candidate region if Pi is lower than 0.5;
generating a taxonomy branch, Pci=FC4[FC3[Pooling(f,Ri′)]];
generating a mask branch, Mi=FC6[FC5[Pooling(f,Ri′)]];, wherein, y is an output of the residual network, x is an input of the residual network, F is a convolution function, f is an image feature outputted by the residual network, Ri is the candidate region, Pooling is pooling, FC1 and FC2 are first and second fully connected layers, respectively, Pi is probability of candidate region Ri belonging to a foreground (i.e., the region containing the object to be recognized), Ri′ is kept candidate region, FC3 and FC4 are third and fourth fully connected layers, respectively, Pci is probability of the object c to be recognized in the candidate region Ri′, FC5 and FC6 are fifth and sixth fully connected layers, respectively; a matrix Mi has pixel size identical to the candidate region Ri′, and each position in the Mi represents a probability that the pixel point belongs to the recognized object in the candidate region.