Cubist-Inspired Deep Learning with Sonar for UXO Detection and Classification
Abstract
The objective of this project was to demonstrate a proof-of-concept regarding the feasibility of using convolutional neural networks (CNNs) for unexploded ordnance (UXO) classification. Within this context, one main strand of work focused on assessing the applicability of two forms of transfer learning for the task of underwater object classification: target-concept transfer and sensor transfer. The use of transfer learning would allow data collected during mine countermeasures operations, and from different but similar sensors, to be leveraged for the UXO problem. The other main task of the project was to develop a CNN framework that could exploit multiple representations of sonar data simultaneously. The idea underlying the use of multiple representations (derived from the same raw data) is that complementary classification clues would be made accessible in different representations. Experiments involving measured sonar data demonstrated the successful fulfillment of the project objectives.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 16, 2019
- Accession Number
- AD1135275
Entities
People
- David P. Williams