International Journal of New Developments in Engineering and Society, 2019, 3(2); doi: 10.25236/IJNDES.19238.
Dong Han, Gang Tang
Shanghai Maritime University, Shanghai, China
In order to detect multi-damage types of quayside crane simultaneously, a visual detection method based on region proposal deep network is proposed. Aiming at the difference of damage types and size of quayside bridges and the large distribution of small targets, the feature pyramid network (FPN) method is used to increase the resolution of feature mapping and to detect larger cracks and smaller corrosion of quayside bridges by using high-level semantic information after top-down model. By using mixup data enhancement method, the generalization ability of the original Faster R-CNN model structure can be reduced, the memory of error labels can be reduced, and the robustness against samples can be increased. Experiments show that the improved Faster R-CNN improves the detection of mAP by 5.14%.
Faster R-CNN; Damage detection; Characteristic pyramid network; Mixing up.
Dong Han, Gang Tang. Damage detection of quayside crane structure based on improved Faster R-CNN. International Journal of New Developments in Engineering and Society (2019) Vol.3, Issue 2: 284-301. https://doi.org/10.25236/IJNDES.19238.
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