R-CNN Auto-system for Detecting Text Road Signs in Baghdad

다국어 초록 (Multilingual Abstract)

Due to inadequate lighting, motion blur, occlusion, and the eventual disappearance of road signs, the determination of textual road signs is difficult to resolve. With the aid of a recurrent convolutional neural network (R-CNN), the current study focuses on detecting textual road signs in Baghdad at different times of day under varied situations, including vehicle speed, surrounding layers, epochs of the R-CNN, etc. Two types of different contrast on signs were used: blue and blue-green signs with white text. The differences in contrast seem to play an effective role in recall, sensitivity, and F1 score values. Results showed that the precision values for all signs and epochs were unity. For 20 and 60 epochs, the sensitivity values for the blue sign were 47.43% and 48.35%, respectively, while for the blue-green sign, the sensitivity values were equal to 95.19% for both numbers of epochs. The F1 scores were 0.6435 and 0.9753 for 20 epochs, while for 60 epochs it was 0.6518 and 0.9753 for blue and blue-green signs, respectively. The experiments validated the suggested software and provided implementation guidance to diagnose and automatically classify text road signs on streets.