Performance comparison of neural networks for undersea mine detection

Scott T. Toborg; Matthew Lussier; David Rowe

doi:10.1117/12.169967

2 March 1994 Performance comparison of neural networks for undersea mine detection

Scott T. Toborg, Matthew Lussier, David Rowe

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169967
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

This paper describes the design of an undersea mine detection system and compares the performance of various neural network models for classification of features extracted from side-scan sonar images. Techniques for region of interest and statistical feature extraction are described. Subsequent feature analysis verifies the need for neural network processing. Several different neural and conventional pattern classifiers are compared including: k-Nearest Neighbors, Backprop, Quickprop, and LVQ. Results using the Naval Image Database from Coastal Systems Station (Panama City, FL) indicate neural networks have consistently superior performance over conventional classifiers. Concepts for further performance improvements are also discussed including: alternative image preprocessing and classifier fusion.

Citation Download Citation

Scott T. Toborg, Matthew Lussier, and David Rowe "Performance comparison of neural networks for undersea mine detection", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169967

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