To improve the recognition accuracy of pavement crack target detection robots based on image processing, this research proposes a road crack recognition model built on the improved YOLOv4 algorithm. The enhanced YOLOv4 model, which utilizes optimized K-means and attention mechanisms for improved detection, is combined with a binocular vision calibration method for accurate camera setup. Feature points are extracted and matched using an accelerated robust feature algorithm to measure crack widths, resulting in a novel binocular vision-based road crack detection system. The experiment demonstrated that the detection accuracy of the proposed enhanced YOLOv4 exceeded 97% on datasets, with recall, F1, AUC, and precision values of 0.973, 0.986, 0.985, and 0.989, respectively. The accuracy of the research model exceeded 98%, and the fit with the sample data exceeded 96%. The research methods can effectively achieve intelligent and accurate detection of road cracks, which has positive significance for road maintenance work.