Previous efforts have demonstrated that neural networks can form the basis of an automated damage assessment system which can detect, locate, and categorize explosive detonations. However, the network configurations currently in use for damage assessment do not provide the accuracy required in a deployable system. Further, the current configurations suffer considerable performance degradation in both speed and accuracy when multiple detonations occur in rapid succession. This proposal suggests research which will offer significant performance gains in both speed and accuracy for neural networks in damage assessment.
Keywords: DAMAGE ASSESSMENTS NEURAL NETWORKS PATTERN RECOGNI REAL TIME DECISION MAKING DIGITAL COMPUTE SENSORS