Fast QR code detection based on BING and AdaBoost-SVM

In industry 4.0, the most popular way to identify and track objects is to add tags. Because the cost of smart tag is still high, most companies still use cheap barcodes or QR codes. In order to solve the real-time positioning problem of upgrading traditional tags into smart tags, this paper proposes a QR tag location method based on Binarized Normed Gradients (BING) and AdaBoost-SVM. BING algorithm is the fastest general object detection algorithm at present, but its disadvantage is that the recall rate decreases sharply with the increase of Intersection-over-Union (IoU) threshold. In the proposed algorithm, Adaboost-SVM method is introduced to make up for the shortcomings of BING algorithm. More specifically, before training and prediction process of Adaboost-SVM, Contrast Limited Adaptive Histogram Equalization (CLAHE) mechanism is used for image enhancement, and thus can greatly shorten the training time and improve the precision of prediction. As a result, the precision of low-quality image prediction is significantly improved. Compared with the existing methods based on Neural Network (NN), the proposed algorithm does not depend on the parallel acceleration hardware such as GPU, hence, it can significantly reduce the hardware cost, and expand QR code automatic positioning algorithm utilization in the field of lower hardware requirements.