Patent ID: 11971726
Assignee: WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 2:
3. The method according to claim 1, wherein
in 2), the semantic segmentation data set is as follows:

I={datam(u,v),typem(u,v)},m∈[1,M],u∈[1,U1],v∈[1,V1]

where, M refers to a number of non-wall corner sample images in the semantic segmentation data set I, UI refers to a number of columns of each non-wall corner sample image in the semantic segmentation data set I, VI refers to a number of rows of each non-wall corner sample image in the semantic segmentation data set I, datam(u,v) refers to a pixel in column u and row v of the m-th non-wall corner sample image in the semantic segmentation data set I, and typem(u,v) refers to a category of the pixel in column u and row v of the m-th non-wall corner sample image in the semantic segmentation data set I;
in 2), the DeepLab v2 network loss function is a cross-entropy loss function;
in 2), the optimized DeepLab v2 network is obtained through optimization training, comprising:
minimizing the cross-entropy loss function as an optimized target, and
obtaining the optimized DeepLab v2 network through optimization based on a SGD(Stochastic Gradient Descent) algorithm;
in 2), the target detection data set is as follows:

C={datap(x,y),(xp,nl,u1,yp,nl,u1,xp,nr,d,yp,nr,d,Typ,n,s)},

p∈[1,P],x∈[1,X],y∈[1,Y],n∈[1,Np]

where, P refers to a number of wall corner sample images in the target detection data set, X refers to a number of columns of each wall corner sample image in the target detection data set, Y refers to a number of rows of each wall corner sample image in the target detection data set, NP refers to a number of rectangular bounding boxes in a p-th wall corner sample image in the target detection data set, and s refers to a category of a pixel point; datap(x,y) refers to a pixel in column x and row y in the p-th wall corner sample image in the target detection data set, xp,nl,u1 refers to an abscissa of an upper left corner of the n-th rectangular bounding box in the p-th wall corner sample image in the target detection data set, yp,nl,u1 refers to an ordinate of the upper left corner of the n-th rectangular bounding box in the p-th wall corner sample image in the target detection data set, xp,nr,d refers to an abscissa of a lower right corner of the n-th rectangular bounding box in the p-th wall corner sample image in the target detection data set, yp,nr,d refers to an ordinate of the lower right corner of the n-th rectangular bounding box in the p-th wall corner sample image in the target detection data set, and Typ,n,s refers to an object type in the n-th rectangular bounding box in the p-th wall corner sample image in the target detection data set;
in 2), the SSD network loss function consists of a log loss function for classification and a smooth L1 loss function for regression;
in 2), the optimized SSD network is obtained through optimization training, comprising:
obtaining the optimized SSD network through optimization based on the SGD algorithm.