Patent ID: 11915477
Assignee: KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 2:
3. A video processing method comprising operations of:
(A) allowing a central control unit of a central server to transmit a signal for requesting video information of a fire site to each of multiple unmanned aerial vehicles (UAVs) through a central transceiving unit, each UAV comprising a control unit including an input layer and a first hidden layer of a deep neural network;
(B) allowing a control unit of each of the UAVs to extract a feature map obtained by distorting acquired video of the fire site by providing pixel information of the video to the input layer and executing a convolution operation using a filter via the first hidden layer and transmit the feature map to the central server;
(C) allowing the central control unit of the central server to sum and regenerate the feature map by performing a convolution operation on the summed feature map, the central control unit comprising multiple hidden layers and an output layer of the deep neural network;
(D) allowing the central control unit to perform computation by substituting the regenerated feature map into an activation function;
(E) allowing the central control unit to execute a pooling operation using a value output using the activation function;
(F) allowing the central control unit to compute a loss value using a pooling operation value and a preset target output value;
(G) allowing the central control unit to acquire a modified value for a parameter using the computed loss value and update the parameter using the acquired modified value for the parameter; and
(H) allowing the central control unit to re-execute operations (C) to (G) using the updated parameter,
wherein the control unit of each UAV separately comprises the input layer and the first hidden layer of the deep neural network, and the central control unit of the central server separately comprises the multiple hidden layers and the output layer of the deep neural network.