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Intersection is an important traffic node in the road network, which is prone to traffic congestion. In order to improve the efficiency of special vehicles while ensuring traffic safety, a machine vision based rapid traffic technology for special vehicles at intersections is studied. The optimization of traffic foundation adopts image acquisition and preprocessing, detection and recognition, and traffic control as the technical framework structure. Machine vision technology is used to collect real-time traffic images at intersections, and initial image preprocessing is achieved through image filtering, image enhancement, and other steps. Using the Car-YOLO network to identify the traffic capacity of intersections, plan fast traffic routes, consider the driving status of the preceding vehicle, calculate the speed of special vehicles, and control the intersection signal lights based on the lane occupied by vehicles, such as early green light and extended green light cycle time, to achieve rapid passage of special vehicles at intersections. The experimental results show that in congested and normal traffic scenarios, the average passing time of special vehicles with optimized design technology is 18.2 seconds and 10.1 seconds, respectively, and the probability of accidents is less than 2% and 1.4%, respectively. It has good application effects.