Author Affiliations
1 1School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China2 2School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, Chinashow less
Fig. 1. Symbolic characteristics of QR codes. (a) Structure of QR codes; (b) detection patterns
Fig. 2. Results comparison of QR codes binarization. (a) Original images of QR codes; (b) Otsu algorithm; (c) the proposed algorithm
Fig. 3. Search the central point of detection pattern. (a) Line scan; (b) column scan; (c) adjust to the center position; (d) judge the relationship between S1 and S2
Fig. 4. Position relationship of QR codes in the image
Fig. 5. Search of QR codes vertices. (a) Position analysis of detection pattern points; (b) contour tracing start point; (c) vertices of detection patterns; (d) the fourth vertex
Fig. 6. Inverse perspective transformation
Fig. 7. Correction results in different environments. (a) Normal illumination; (b) low light environment; (c) complex background environment
Fig. 8. Correction results of fuzzy image. (a) Light fuzzy; (b)(c) fuzzy
Fig. 9. Analysis of different algorithms. (a) Ref. [5] algorithm; (b) Ref. [13] algorithm; (c) Ref. [8] algorithm
Algorithm | Average execution time /ms | Recognition rate /% |
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Group 1 | Group 2 | Group 3 | | Group 1 | Group 2 | Group 3 |
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Ref. [9] algorithm | 96.879 | 63.776 | 79.216 | 85 | 78 | 77 | Proposed algorithm | 32.497 | 22.328 | 27.164 | 92 | 90 | 88 |
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Table 1. Comparison of computational time and recognition rate of multi QR codes correction algorithms
Algorithm | Average execution time /ms | Recognition rate /% |
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Group 1 | Group 2 | Group 3 | | Group 1 | Group 2 | Group 3 |
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Ref. [5] algorithm | 37.623 | 36.879 | 38.264 | 70 | 61 | 43 | Ref. [8] algorithm | 34.423 | 32.667 | 37.213 | 82 | 76 | 64 | Ref. [13] algorithm | 29.414 | 27.016 | 28.864 | 84 | 78 | 51 | Proposed algorithm | 11.213 | 10.464 | 11.625 | 93 | 91 | 89 |
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Table 2. Comparison of calculation time and recognition rate of single QR code correction algorithms