In this study, we propose a method for recognizing numerical road signs on noisy images using wavelet differentiation. The proposed method consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the input image is first converted to grayscale and then filtered using a median filter to remove noise. In the feature extraction stage, wavelet differentiation is used to extract features from the preprocessed image. Finally, in the classification stage, a support vector machine (SVM) classifier is trained to classify the extracted features into different classes corresponding to different numerical road signs.
Experimental results on a dataset of real-world images show that the proposed method achieves high recognition rates even on noisy images with low contrast and illumination variations. The proposed method outperforms other state-of-the-art methods in terms of recognition accuracy and robustness to noise.
In conclusion, the proposed method provides a promising solution for recognizing numerical road signs on noisy images, which has important applications in intelligent transportation systems and autonomous driving.
- Developer Богдан Рябоконь
- Doesn't sleep at night to make everything work Мария Ловушко
- Author of the image processing algorithm Светлана Швидченко