Traffic Sign Recognition Using Artificial Neural Network

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Traffic Sign Recognition Using Artificial Neural
Network
101100

• Radi Bekker

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Motivation for ANN

• von Neumann machines are based on the processing
  one processing unit, many operations in one
  second.
• Neural networks are based on the parallel
  architecture of animal brains-slow ,parallel and
  complicated-good for pattern matching.
• Pattern matching can solve many problems to which
  algorithms are not exist or very complicated.

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The human brain

• Consists from 1011 neurons
• Neurons are connected by around 1015 connections
  .
• Neurons send impulses to each other through the
  connections and these impulses make the brain
  work.
• Dendrites- responsible for input.
• Axon- responsible for output.

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Artificial neural network (ANN)

• Network is constructed from artificial neuron
  layers.
• There is input and output layers and any number
  of hidden (internal) layers.
• Each neuron in one layer is connected to every
  neuron in the next layer.

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Artificial Neuron

• Many inputs like dendrites.
• One output like axon.
• Each neuron receives a signal from the neurons in
  the previous layer.
• The weighted inputs are summed, and passed
  through a limiting function which scales the
  output to a fixed range of values.
• The output of the limiter is then broadcast to
  all of the neurons in the next layer.

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Training- Back Propagation-1

• The most common learning algorithm is called Back
  Propagation (BP).
• A BP network learns by example, that is, we must
  provide a learning set that consists of some
  input examples and the known-correct output for
  each case.
• This method adjusts the weights between the
  neurons to solve a particular problem.
• The BP learning process works in small iterative
  steps one of the example cases is applied to the
  network, and the network produces some output
  based on the current state of it's synaptic
  weights.
• This output is compared to the known-good output,
  and a mean-squared error signal is calculated.

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Training- Back Propagation-2

• The error value is then propagated backwards
  through the network, and small changes are made
  to the weights in each layer.
• The whole process is repeated for each of the
  example cases, then back to the first case again,
  and so on.
• The cycle is repeated until the overall error
  value drops below some pre-determined threshold.
• At this point we say that the network has learned
  the problem "well enough" .

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My Network

• Input layer-10,000 neurons.
• Hidden layers-3 hidden layers with 10 neurons
  each.
• Output layer-16 neurons for 16 traffic signs.
• Training- network trained for 2000 cycles.

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Image Filtering

• Resizing the image to size 100x100.
• Turning the image to black and white.
• Rescaling the matrix image to numbers between 0
  and 1.
• Constructing a 10,000 sized vector from the
  columns of the image matrix.

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Results

• Good results for trained images
• Bad results for real picture images.
• When the network was constructed to identify 5
  images- better results was achieved.
• Contrast and brightness adjustments in some cases
  contributed to sign correct recognition.

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Conclusions

• ANN is good for small problems and networks.
• ANN is bad for big networks.
• Bigger network more training time needed.
• Hard to find out good network configurations.
• ANN is a good method for solving hard
  computational problems.
• More research on human brain could be helpful in
  constructing better ANN.