Traffic Sign Visual Recognition Study Based on full-reference Image Quality Assessment Algorithms

The factors influencing the visual recognizability of traffic signage are diverse. To study the synergistic effects and critical value models of these visual recognition elements, it is necessary to conduct experiments and collect data on the recognition influence factors. However, data collection based on human testers is limited by experimental conditions, making it difficult to establish large-scale datasets and avoid individual errors.The full-reference algorithm for image quality evaluation is a technique used in the field of computer image recognition to identify image distortions and assess distortion scores. Therefore, this study attempts to apply this method in cross-modal transfer learning, simulating the clarity judgment of drivers for images representing different presentations of multivariable elements. By varying three variables—font height, weight, and recognition distance—this approach simulates signage presentation images at different recognition distances. Using a Back propagation neural network (BPNN) to model signage recognizability and the computer Zernike moment algorithm to simulate recognition for given variable settings, large-scale calculations are performed to analyze the regression curves of recognition variables and compare them with data from human testers.The results show that using computational algorithms to process images with different variables helps analyze the critical points of these variables, significantly improving the accuracy and efficiency of the research. It reduces the individual differences in subjective judgment and can be applied to future studies on visual recognizability experiments.The findings evince that the utilization of computer algorithmic processing of images formulated via distinct variables facilitates the identification of critical points within the variable space, thereby markedly enhancing the precision and efficacy of research endeavors while concurrently mitigating the inherent subjectivity endemic to individual judgments. This paradigm holds promise for future research endeavors germane to visual recognition experimentation.