SBIR-STTR Award

Traditional techniques to detect and remove landmines are both dangerous and time consuming. Very little technology is currently employed in the real world for the detection of landmines. Metal detectors are effective against metal-based landmines, but many mines are plastic cased. Landmines are divided up into two broad classes: antitank (AT) mines, which are designed to impede the progress of or destroy vehicles, and antipersonnel (AP) mines, which are designed to kill and maim people. Landmine comes in a variety of shapes and sizes. They can be square, round, cylindrical, or bar shaped. The casing can be metal, plastic, or wood. These characteristics make the landmine detection challenging. An effective mine detection system should be capable of using all available thermal, spectral and spatial differences for discrimination. The main objective of this project is the development and implementation of a new landmine detection system with classifiers based on surface shapes, textures, comprehensive feature vectors and spectral structures. We propose to develop a set of new nonparametric hypothesis test schemes based on cluster trending analysis and randomness test. A new unsupervised neural network is proposed to cluster measurement data. All classifiers will be fused based on our LIM-based optimal fusion method

Traditional landmine detection techniques are both dangerous and time consuming. Landmines can be square, round, cylindrical, or bar shaped. The casing can be metal, plastic, or wood. These characteristics make landmine detection challenging. In Phase I, we have developed new methods that improve the performance of both surface and buried mine detection. Extensive tests using actual MWIR images have shown extremely promising results. In Phase II, we will conduct further tests using additional ASTAMIDS data sets. Algorithms developed in Phase I will be further improved, which include an innovative image enhancement algorithm, new buried mine classifiers, better contour extraction method, and LMI based soft fusion. The improved system will be further adapted for IED detection through discriminating buried command wires using IR images collected at the field. We will also develop a commercial product for natural gas pipeline leak detection. Our Phase II prototype system is a combination of both software and hardware with various I/O interfaces to imaging sensors and existing ASTAMIDS systems. All algorithms will be implemented in C/C++ and a sophisticated graphical user interface will be developed in Microsoft VB.NET. Our system can be deployed at either aerial or ground platforms with near real time detection capabilities.

Keywords:
Surface Mine Detection; Buried Mine Detection; Ied Detection; Classifier; Fourier Descriptor; Moment Invariant; Image Enhancement; Linear Matrix Inequ