Preprocessing of traffic sign images based on improved histogram equalization and bilinear interpolation

Abstract

In real-world scenarios, due to the complexity of the outdoor environment, motion blur, and the geometric features of the signs themselves, the obtained traffic sign images often exhibit severe distortion, which has a negative impact on subsequent feature extraction and classification. Therefore, this article proposes Canny's traffic sign edge extraction algorithm based on bilinear interpolation improvement. Firstly, in order to reduce the actual consumption of image processing and recognition in the later stage, this article adopts the weighted average method to process the grayscale of the original color image; Secondly, in order to reduce the impact of light intensity on image quality, this article adopts a histogram equalization image enhancement method; Then, in order to avoid the impact of scale on subsequent feature extraction and classification, this paper uses bilinear interpolation to normalize the image; Finally, by improving the Canny edge detection algorithm, the problem of limited edge detection for long-distance traffic signs is solved, thereby accurately detecting edges and improving efficiency. These preprocessing steps can effectively improve image quality and improve the accuracy of subsequent feature extraction and classification.