Speedy location of 2D barcode region under complicated metal background

Current detection algorithms include geometric method and texture analysis method. In locating 2D code under various material backgrounds, especially under metal background, geometric method is characterized by poor robustness and texture analysis method by slow processing speed. To solve the drawbacks mentioned above, the integration of machine learning method into cascade filter method is proposed in this paper to filter background areas, then the geometric properties of 2D barcode are used to detect candidate target area, and finally clustering growth method is employed to envelope 2D barcode region. The experiments reveal that, compared with traditional methods, the method proposed in this paper has achieved higher detection rate with better robustness. With the trained cascade classifier and the connected region classifier, the average positioning accuracy of 97% can be achieved and the processing time can be controlled within 700 ms, which has a great value in obtaining reliable information of 2D barcode on metal parts.