1. A method for instant recognition of traffic lights countdown image, comprising: retrieving information of a real-time image that is divided into a plurality of partitions, each having the four corners as confirmed pixels which are processed within HSL (hue, saturation, lightness) color space; scanning the HSL color features of the confirmed pixels and when the color features conform to predetermined ones, rendering the confirmed pixels as candidate pixels which turn the neighboring four partitions into candidate regions; searching the neighboring confirmed pixels of the candidate pixels that resemble the features thereof and render them as candidate pixels as well until every neighboring confirmed pixel is checked, and merging all the candidate pixels and the candidate regions thereof together as a group; conversing the merged candidate regions within HSL color space by adjusting the threshold value of the lightness of said merged candidate regions with adaptive threshold algorithm, conversing into a binary image of lightness, and then conversing the binary image of lightness into an edge image of lightness after edge detection processing, then intersecting the edge image of lightness and a binary image of hue conversed from the candidate regions in accordance with its range of color, and producing an edge image which has a feature of circular image found after Hough transform algorithm operation and to be compared with a predetermined circular shape of a traffic light; confirming the circular feature as the shape of a traffic light and then retrieving a countdown image by calculating the displacement ratio from the circular image; enhancing said countdown image by super resolution algorithm and conversing into a greyscale image, then adjusting the threshold value thereof by adaptive threshold algorithm and conversing into an image of binary numbers; gathering horizontal and vertical projection information of the image of binary numbers, finding a threshold value of the top and bottom edge thereof and figuring out a width and estimated cutting curve thereof, and then cutting the image of binary numbers along the cutting curve calculated by partial distribution statistics of the vertical projection information and the estimated cutting curve, then applying the block coding algorithm to display a feature image by dividing the image of binary numbers into equal rectangular blocks, calculating the ratio of black pixels and white pixels of each rectangular block, and encoding the results; and classifying and concluding all collected images of numbers by a plurality of classifiers with machine learning algorithm to analyze and compare with the feature image, and calculating the percentage of similarity, then bringing out the image of the highest percentage as the recognition result among the ones from the classifiers.