Abstract
Airport pavement inspection is an important link to ensure the safe takeoff and landing of aircraft. At present, the airport pavement safety inspection is still dominated by manual inspection. This method has some problems such as low detection efficiency, strong subjectivity, and unable to fully cover. In this context, Faster RCNN network was used for tunnel crack detection, the backbone network of Faster RCNN was modified, the lightweight network MobileNetV2 was used to improve the network training speed, and CBAM attention mechanism was added to improve the feature extraction ability of the model.
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