Automatic Target Detection and Cuing System for an Autonomous Underwater Vehicle (auv)

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Abstract
This paper presents the technology needed for searching large ocean areas with imaging sonars or video cameras. To search a large area in reasonable time, multiple autonomous underwater vehicles (AUVs) may be used to cover contiguous parts of the search area. This autonomous search capability allows operating personnel to be removed from hazardous environments, provides economy through reduction in the number of required host ships, and reduces search time. The technical approach presented here uses both algorithms and neural networks to build a target detection and classification system. This system is robust in that it continues correct operation even with multiple failures. Hardware implementations of the system allow processing at video and ultra high resolution sonar rates. Both sonar and video images are created from either side scan sonar or video camera using 8 bit grey level pixel representations of the reflected energy. Objects are detected by searching for highlight, shadow, texture changes or statistical anomalies in the image. A neural network is also trained to recognize image features and it is used to complement the performance of the algorithmic detectors. A neural network (NN) has been developed for classifying target scenes which are defined as specific sets of man-made and natural features. The NN classifier has been trained to recognize the target scene from 360 degree aspect angles. Detector and classifier performance are evaluated using both our synthetic and real image libraries. We present our current laboratory hardware for testing and training both the algorithms and neural networks. Hardware for real time execution is also discussed.

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