A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

摘要

Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a Back-Propagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the δ-technique. Our results show that the combination of the δ-technique and the S-statistics with a neural network improves the performance.