Signage recognition is one of the hot topics in recent years. It has important applications in intelligent traffic and autonomous driving of smart cars. This paper designs a road marking recognition method combining OPSA point set distance and wavelet transform. The method consists of three main phases: 1) image denoising, restoration, 2) feature extraction, and 3) image recognition. First, a Gaussian-smoothing filter used to attenuate or remove irrelevant information in the image, enhance related information in the image, and achieve image denoising. In the feature extraction stage, the feature extraction and recognition method based on wavelet transform adopted to overcome the deficiency of the traditional Fourier feature extraction method, ensure that high frequency information is not lost, and low frequency information is not lost. Finally, the OSPA point set used to identify distance markers. Compared with the standard image, the experimental results show that this method can overcome the weather change, Gaussian white noise caused by illumination changes, and the slight rotation of the collected landmark image, the scale change and the noise caused by the translation. This method realizes the accurate recognition of road signs, has strong fault tolerance and robustness, and has certain guiding significance for the research of assisted driving systems.