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Computer built on an FPGA automates fish inspection on-board ship

Matrox VITE : 08 July, 2007  (New Product)
To avoid spoilage, modern commercial fishing vessels carry on-board facilities for processing their catch. Human labor is at a premium, so commercial fishing concerns install automation whenever possible.
One task that has resisted automation, however, is ensuring that only high-quality fish go into the filleting machines and that they go into the machines head first; otherwise, they would be ruined in the machine.

“Someone has to look at every fish as they come below deck to the filleting machine,” says Steven Hoffman, director of research at Pisces VMK. “They have to check for correct orientation and correct species and pull out any damaged fish.”

Pisces VMK manufactures and installs processing equipment for commercial fishing fleets, so it is knowledgeable about both the advantages automation provides to commercial fishing fleets and the challenges it presents in designing the equipment. One of the biggest challenges is ensuring that the equipment is not only fool-proof, but easy to operate and maintain at sea, where there are no technical experts available to sort out problems with the equipment.

Fish are not parts manufactured on an assembly line but are natural objects that come in all sorts of sizes and shapes, and they enter the processing line in all possible orientations. Even when fishermen are targeting a particular species, the variations between acceptable and unacceptable fish is more than conventional vision systems can accurately process.

Pisces asked General Vision to develop a system to help automate the inspection process. GV develops image-recognition systems based on artificial intelligence and zero instruction set computing. A ZISC processor is a neural network made up of a large number of simple processors. Each processor, called a neuron, compares a number of input values to a stored pattern, then passes the result on to other neurons. Those neurons process it along with other inputs; working together, the neurons collectively produce a decision.

AI solutions “learn” by experience. The learning process involves a teacher presenting the AI application with examples of sensory input patterns-that is, images-along with the correct classifications for each. The application then stores the example/category pairs in memory for future reference. When presented with a new target, the system can compare the target with its stored examples, pick out the closest match, and read out its classification. As the application gains more examples, it becomes better able to find a correct match, and its ability to correctly categorize new targets asymptotically approaches 100%.

In the Pisces case, the inputs derive from a digital camera, and the output is a classification of the subject fish into: accept, recycle, reject, or empty. An “accept” result says that the processor thinks the fish is the right species, within the acceptable size range, undamaged, and in the right orientation. A “recycle” result says the fish is perfectly fine, but moving down the line tail first, so it should be turned around and sent back. A “reject” result says that there is something (anything) wrong with the fish itself, and it should be dumped into the fish-meal grinder. An “empty” result means that there was no fish on the line.

GV created CogniSight Image Knowledge Builder software, a simulation of a ZISC processor running on a laptop computer. After the simulation has been tested, it designs a hardware implementation programmed into the gates of a field-programmable gate array.

During training, CogniSight IKB automatically develops a neural net simulation specifically tuned to classify the fish likely to be found in a boat’s catch at the time and place it was caught. Part of the teaching process is to iteratively tune the simulation using additional images to achieve very high (better than 95%) accuracy under the use conditions.

This simulated neural net can be, and sometimes is, used directly to process fish. Programming it into an Actel ProAsic3 FPGA, however, makes it run much faster-exceeding 60 frames/s. This speed advantage accrues from the neural net’s parallelism. Image information appearing at the neurons’ inputs propagates to the outputs at very high, appearing as a low-voltage digital signal at the output.
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