REAL TIME DETECTION AND CLASSIFICATION OF TRAFFIC SIGNS BASED ON YOLO VERSION 3 ALGORITHM

The issue of effective detection and classification of various traffic signs is studied. The two-stage method is proposed for creation of holistic model with end-to-end solution. The first stage includes implementation of effective localization of traffic signs by YOLO version 3 algorithm (You Only Look Once). At the first stage the traffic signs are grouped into four categories according to their shapes. At the second stage, an accurate classification of the located traffic signs is performed into one of the forty-three predefined categories. The second stage is based on another model with one convolutional neural layer. The model for detection of traffic signs was trained on German Traffic Sign Detection Benchmark (GTSDB) with 630 and 111 RGB images for training and validation, respectively. Сlassification model was trained on German Traffic Sign Recognition Benchmark (GTSRB) with 66000 RGB images on pure “numpy” library with 19 × 19 dimension of convolutional layer filters and reached 0.868 accuracy on testing dataset. The experimental results illustrated that the training of the first model deep network with only four categories for location of traffic signs produced high mAP (mean Average Precision) accuracy reaching 97.22 %. Additional convolutional layer of the second model applied for final classification creates efficient entire system. Experiments on processing video files demonstrated frames per second (FMS) between thirty-six and sixty-one that makes the system feasible for real time applications. The frames per second depended on the number of traffic signs to be detected and classified in every single frame in the range from six to one.