A method for optimizing a neural network, comprising: capturing, via a first sensor group having a first field of view, a first sample set having a first sensor domain corresponding to the first field of view; capturing, via a second sensor group having a second field of view, a second sample set having a second sensor domain corresponding to the second field of view, wherein the second field of view overlaps at least a portion of the first field of view; generating one or more regions of interest of the second sample set; translating the one or more regions of interest from the second sensor domain to the first sensor domain; identifying one or more nodes of the neural network which correspond to the one or more translated regions of interest, wherein each node has a weight value; and optimizing the neural network by at least one of: increasing the weight value of the nodes corresponding to the one or more translated regions of interest; and decreasing the weight value of the nodes not corresponding to the one or more translated regions of interest; wherein the nodes of the neural network corresponding to the translated regions of interest are identified by: feeding a binary input matrix into the neural network; and detecting the nodes of the neural network which are activated by feeding the binary matrix; and wherein the binary input matrix is equal in dimension to the first sample set; wherein the binary input matrix comprises one or more zero entries corresponding to the areas outside of the translated regions of interest; and wherein the binary input matrix comprises one or more non-zero entries corresponding to the translated regions of interest. capturing, via a first sensor group having a first field of view, a first sample set having a first sensor domain corresponding to the first field of view; capturing, via a second sensor group having a second field of view, a second sample set having a second sensor domain corresponding to the second field of view, wherein the second field of view overlaps at least a portion of the first field of view; generating one or more regions of interest of the second sample set; translating the one or more regions of interest from the second sensor domain to the first sensor domain; identifying one or more nodes of the neural network which correspond to the one or more translated regions of interest, wherein each node has a weight value; and optimizing the neural network by at least one of: increasing the weight value of the nodes corresponding to the one or more translated regions of interest; and decreasing the weight value of the nodes not corresponding to the one or more translated regions of interest; wherein the nodes of the neural network corresponding to the translated regions of interest are identified by: feeding a binary input matrix into the neural network; and detecting the nodes of the neural network which are activated by feeding the binary matrix; and wherein the binary input matrix is equal in dimension to the first sample set; wherein the binary input matrix comprises one or more zero entries corresponding to the areas outside of the translated regions of interest; and wherein the binary input matrix comprises one or more non-zero entries corresponding to the translated regions of interest. increasing the weight value of the nodes corresponding to the one or more translated regions of interest; and decreasing the weight value of the nodes not corresponding to the one or more translated regions of interest; wherein the nodes of the neural network corresponding to the translated regions of interest are identified by: feeding a binary input matrix into the neural network; and detecting the nodes of the neural network which are activated by feeding the binary matrix; and wherein the binary input matrix is equal in dimension to the first sample set; wherein the binary input matrix comprises one or more zero entries corresponding to the areas outside of the translated regions of interest; and wherein the binary input matrix comprises one or more non-zero entries corresponding to the translated regions of interest. feeding a binary input matrix into the neural network; and detecting the nodes of the neural network which are activated by feeding the binary matrix; and wherein the binary input matrix is equal in dimension to the first sample set; wherein the binary input matrix comprises one or more zero entries corresponding to the areas outside of the translated regions of interest; and wherein the binary input matrix comprises one or more non-zero entries corresponding to the translated regions of interest.