Fish Inspection System Using a Parallel Neural Network
A generic image learning system, CogniSight®, is being used for the inspection of fishes before filleting off-shore. More than thirty systems have been deployed on seven fishing vessels in Norway and Iceland over the past three years. Each CogniSight uses four neural net-work chips (a total of 312 n...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.583.5206 http://www.aaai.org/Papers/AAAI/2007/AAAI07-280.pdf |
| Summary: | A generic image learning system, CogniSight®, is being used for the inspection of fishes before filleting off-shore. More than thirty systems have been deployed on seven fishing vessels in Norway and Iceland over the past three years. Each CogniSight uses four neural net-work chips (a total of 312 neurons) based on a natively parallel hardwired architecture performing real time learning and non-linear classification (RBF). These sys-tems are trained by the ship crew using Image Knowl-edge Builder, a ”show and tell ” interface for easy train-ing and validation. Fishermen can reinforce the learning at anytime when needed. The use of CogniSight has re-duced significantly the number of crewmembers on the boats (by up to six persons) and the time at sea has shortened by 15%. The prompt and strong return of the investment to the fishing fleet has increased signifi-cantly the market shares of Pisces Industries, the com-pany integrating CogniSight systems to its filleting ma-chines. |
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