Visual servo control robots mainly rely on their vision systems to detect corner points, edges, and circles of workpieces, providing the basis for subsequent decision-making and control. With the rapid increase of its workload density, the edge modules struggle to meet these detection loads, especially for continuous batch detection in complex scenarios, where the real-time and accuracy of visual system are greatly challenged, reducing the system efficiency. To deal with the above bottlenecks in circle detection, particularly the more challenging circle detection, we proposed a novel synchronous circle detection method based on Hough gradient: the interference information in the image is removed by edge screening of the edge image; the center and radius are synchronously determined by the eight-point method; the radius error is reduced by radius re-search, and invalid calculations are reduced by center position constraints; the accurate detection of circular targets in the image is achieved by candidate circle re-search and optimal circle acquisition. To further enhance the speed and efficiency of circle detection, the above method is integrated with CUDA parallel technology for a synchronous parallel circle detection method based on Hough gradient, which fully utilizes its parallel computing to accelerate the circle detection. In comparison with GHT, CACD, RCD, and Zhao, the proposed method significantly improves the circle detection accuracy and efficiency with stronger anti-noise and anti-disturbance capabilities, where its precision, recall rate and F value are 99.1%, 90.7% and 94.7% respectively; the average detection time is 0.09 s per image with an efficiency increase by up to 26 times, making it suitable for batch image processing in industry.