Gang Li, Qiangwei Liu, Jian Wan, Biao Ma, Ying Li. A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141031
- Laser & Optoelectronics Progress
- Vol. 57, Issue 14, 141031 (2020)
Fig. 1. Flowchart of concrete pavement crack detection
Fig. 2. Concrete pavement crack image dataset. (a) No-crack images; (b) crack images
Fig. 3. Crack images that may be disregarded in detection
Fig. 4. Cutting process diagram for crack images using overlapping sliding window technology
Fig. 5. ILGCHDN classification model structure diagram
Fig. 6. Convolution block of the ILGCHDN model
Fig. 7. Comparison of the renderings by two algorithms after binarization of 6 crack images. (a) Original crack images; (b) label marking images; (c) renderings of the global threshold method; (d) renderings of the adaptive threshold method
Fig. 8. Schematic diagram of calculating crack width in image coordinate system
Fig. 9. Accuracy diagram of the training model
Fig. 10. Loss diagram of the training model
Fig. 11. Error comparison histogram for crack width measurement
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Table 1. Performance comparison table of various crack detection algorithms
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Table 2. Test results of the models on three public datasets
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