A Novel Neural Network based Voting Approach for Road Detection via Image Entropy and Color Filtering

Background: The dramatic increase in the traffic needs effective management that leads to the creation of intelligent transportation systems (ITS) or smart roads. Method: In this paper, a novel and quickly executable neural networks based method is presented to decide the configuration of an adaptive filter and choose among many possible segments of image. Findings: The trained network can be put in practice easily, a number of test image are presented to the software, and the program calculates the multiplication of each scene’s entropy and histogram average and based on that the trained radial basis network predicts the suitable filtering parameter. Result show the outcome of the network trial on three examples. It’s obviously because that the current input has values which are relatively close to the values put at the training stage. Applications/Improvements: A lot of research can be committed about enhanced filtering and masking ideas. A human operator is needed to approve the output’s readiness to be segmented.