US 5153439 A
A nuclear detection system and method uses an artificial neural system to efficiently detect explosives in checked airline baggage or other parcels with a high probability of detection (PD) and a low probability of false alarms (PFA). The detection system detects the presence of nitrogen and other elements and their rough density distribution within the object under investigation by performing a nuclear-based analysis of the object. The detection system includes a source of neutrons; an array of gamma ray detectors; at least one neutron detector; means for irradiating the object being examined with neutrons from the neutron source, which neutrons interact with the atomic nuclei of one or more specific elements, e.g., nitrogen, chlorine, or hydrogen present within the object so as to cause elemental-specific gamma rays to be emitted; means for capturing and counting the elemental-specific gamma rays and determining their approximate origin within the object, thereby providing a rough measure of the density distribution of these specific elements within the object; and means for detecting neutrons that pass through the object without substantially interacting with atomic nuclei, thereby providing an image of the density distribution of elements in the object. The invention further includes an X-radiography system for generating a high-resolution two-dimensional density image of the object. An artificial neural system (ANS) provides an efficient means for analyzing the recorded gamma rays, image, and other data so as to accurately discriminate between objects carrying explosives and objects not carrying explosives.
1. A method for detecting explosives in an object, such as a suitcase or a parcel, comprising the steps of:
(a) irradiating a sample object containing a known explosive material with neutrons having an appropriate energy and intensity to penetrate the walls of the object and to be absorbed into the explosive material within the object;
(b) detecting and recording the number of gamma rays at particular energy levels emitted from the explosive material in the object as a result of the neutron bombardment of step (a);
(c) presenting data derived from the gamma ray counts obtained from the explosive material to an artificial neural system (ANS) for the purpose of teaching the ANS how to properly classify the known explosive material;
(d) irradiating at least one object, such as a parcel or a suitcase, containing unknown materials with neutrons of substantially the same energy and concentration as in step (a);
(e) detecting and recording the number of gamma rays at particular energy levels emitted from the unknown material in the object as a result of the neutron bombardment of step (d); and
(f) presenting data derived from the gamma ray counts obtained from the unknown material to the ANS for the purpose of having the ANS classify the object as one having or not having an explosive material.
2. The method of claim 1 further including repeating steps (a)-(c) a sufficient number of times to teach the ANS how to properly classify a plurality of known explosive materials.
3. The method of claim 2 wherein the repeating of steps (a)-(c) is automatically carried out under control of a computer.
4. The method of claim 1 wherein steps (c) and (f) comprise presenting the gamma ray counts directly to the ANS.
5. The method of claim 1 wherein steps (c) and (f) comprise developing feature data from the gamma ray counts and presenting the feature data to the ANS.
6. The method of claim 5 wherein the step of developing feature data comprises selectively combining the gamma ray counts.
7. The method of claim 6 wherein the step of selectively combining gamma ray counts includes generating three-dimensional image data of at least one particular element within said object, said three-dimensional image data being based on the detection of gamma rays of a particular energy level associated with said at least one particular element.
8. A method of detecting the presence of a prescribed material in an object, such as a suitcas