Traffic sign recognition method, storage medium and processing equipment

Abstract

The invention relates to the field of image recognition, specifically discloses a traffic sign recognition method, a storage medium and processing equipment, and aims at completing the detection and classification of traffic signs in a same module. The traffic sign recognition method comprises the following steps of: extracting maximally stable extremal regions from a normalized RGB channel and anormalized grayscale channel as candidate regions of a traffic sign; carrying out screening on the basis of a preset traffic sign feature condition, zooming the obtained regions to fixed sizes, and decreasing an average value of the preset RGB channel to obtain a fourth group of traffic sign candidate regions; inputting the fourth group of traffic sign candidate regions in a traffic sign recognition network to extract a detection result and a classification result; and removing overlapped regions through a non-maximal suppression algorithm so as to obtain a final recognition result, wherein the final recognition result comprises position, size, specific strategy and confidence coefficient information of the traffic sign. Through the method, the detection and classification of traffic signscan be completed in a same module.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a traffic sign recognition method, a storage medium, and a processing device. Background technique [0002] The recognition of traffic signs plays an important role in assisted driving and unmanned driving systems. However, in real traffic scenes, there are many difficulties in using computers to automatically recognize traffic signs due to factors such as occlusion, illumination, viewing angle, and weather. [0003] Recognizing the traffic signs existing in the whole image is generally divided into two steps, namely the detection and classification of traffic signs. The detection of traffic signs is to locate the signs to be detected from the image, and the specific information includes the position and size of the signs; the classification of traffic signs is to divide the detected signs into their specific categories. [0004] At present, the recognition of traffic signs gen...

AI Technical Summary

Problems solved by technology

However, this method uses different modules for detection and classification, uses various hand-designed features, the training process is complicated, and the hand-designed features are not optimized for specific tasks, the recognition effect of traffic signs is not good, and the results of simultaneous detection To consider the balance between precision and recall

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