I just finished the ANN training and testing including newly added data set. Now our ANN can recognize 62 different types of signs in 82.98% success ratio. As expected, the recognition ratio drops compared to the last week result which could recognize the 33 types of traffic codes in 91.14% ratio.
As we extend the target traffic signs to recognize, the recognition ratio will drop more. For instance, some of newly added target sign sets this week has very similar texture and features with existing ones except the background color.
So it becomes very clear to me that we need to utilize more image features to prevent the recognition ratio from being dropped with getting bigger target signs to recognize. I am now modifying the MUTCD pseudo-color mapping algorithm to consider its neighborhood pixels to find the best matching color and to become the closed region. I wish that this will help us locate the sign from the raw image and improve the color-coded line receptor algorithm.
The attached Excel file includes two sheets of this week result and last week result.
The output of ANN is ordered by its confidence level to recommend a number of candidates for the sign to recognize. The summary of its hit ratio with now multiple candidates to total 1187 samples is:
Please see the updated Excel file for the details. It has now two more sheets including the chart showing the recognition ratio with the increasing number of candidates and ordered ANN outputs. Regarding limitation, let us talk during the meeting hour.