Executive Summary

Recent accomplishments

We have constructed a multi-level classification system for the detection of shallow water mines that includes:

• State of the art image preprocessing and enhancement using:
  • Innovations in computer vision
  • Wavelet denoising
  • Advanced detection techniques that is more robust to background changes
  • Expert fusion at various levels which optimizes computational efficiency vs. local image complexity. It incrementally applies more refined background analysis (computationally intensive).
• Decision support user's interface to enhance the performance of a human experts by overlaying machine expert prediction on the original image.

Recent results

• 100% detection rate!  on simpler and most difficult sonar images (AQS14). Low false alarms (5-8) per image
• The system is near computationally feasible for real-time operation
• It also provides a practical human decision support enhancement via a graphical users interface

6.2, 6.3 Transitions

Overview

In our image enhancement and normalization methods we have succeeded in removing a large portion of background structure resulting from ground vegetation and thus made the task of mine detection much simpler and less computer intensive. This has led to a reduction of an order of magnitude in false alarms and the feasibility of 100% mine detection in difficult images without the need to dynamically adjust the parameters to a new ocean environment.

The results have been achieved by applying an array of experts with different model parameters and image representations. Fusion of these experts is done in such a way that experts which are very computationally expensive are being selectively used for specific regions where their utility is largest.

A side effect of the results is the construction of a graphical users interface which can greatly enhance the work of a human expert by alerting him to specific locations in the image and effectively fusing machine expert information with the sonar image. This project is being transitioned to the AQS 14 effort as well as to unmanned underwater vehicles.

Future goals

• Apply advanced waveform design techniques for pinging mine-like objects
• Enhance acoustic sensors and close range sensors using methods adapted from wavelet packet analysis
• Enhance non-linear feature extraction for discrimination and expert fusion.

Technical Contact