| contributor author | Lebbad, Anderson | |
| contributor author | Clayton, Garrett M. | |
| contributor author | Nataraj, C. | |
| date accessioned | 2022-05-08T08:30:28Z | |
| date available | 2022-05-08T08:30:28Z | |
| date copyright | 3/22/2022 12:00:00 AM | |
| date issued | 2022 | |
| identifier issn | 2689-6117 | |
| identifier other | aldsc_2_3_031003.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4284014 | |
| description abstract | The task of classifying unexploded mortars is critical in both humanitarian and military explosive ordnance disposal (EOD) operations. Classification needs to be completed quickly and accurately and is the first step toward disarming the ordnance because it provides information about the fuzing mechanism, or the stage in the arming cycle that the ordnance is currently in. To assist EOD technicians with mortar identification, this article presents an automated image-based algorithm and the database of images used in its development. The algorithm utilizes convolutional networks with variations to training to improve performance for ordnance found in varying states of disassembly. The classifier developed was found to be 98.5% accurate for these lab condition photos | |
| description abstract | future work will focus on more cluttered environments. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Convolutional Networks for Classification of Mortars | |
| type | Journal Paper | |
| journal volume | 2 | |
| journal issue | 3 | |
| journal title | ASME Letters in Dynamic Systems and Control | |
| identifier doi | 10.1115/1.4053886 | |
| journal fristpage | 31003-1 | |
| journal lastpage | 31003-5 | |
| page | 5 | |
| tree | ASME Letters in Dynamic Systems and Control:;2022:;volume( 002 ):;issue: 003 | |
| contenttype | Fulltext | |
