Concrete dam apparent crack characteristic extraction based on improved DeepLabv3+model
To address the problems of difficulty and poor effect of existing crack detection algorithm caused by complex environment of concrete dams,a characteristic extraction method for the cracks of concrete dams based on the improved DeepLabv3+model was proposed.This method replaced the original backbone net-work with lightweight network to extract image features,which reduced the complexity of the model.The at-rous spatial pyramid pooling module was expanded to widen the encoder receptive field.A multi-scale feature fusion strategy was adopted to improve the utilization of edge information.Moreover,the loss function of the model was optimized to overcome the pixel imbalance.The effectiveness and superiority of the proposed meth-od were verified and evaluated using the self-made concrete dam apparent crack image dataset.The experimen-tal results demonstrate that the proposed network can accurately retrieval characteristics of concrete dam appar-ent cracks under complex background.The intersection over union and pixel accuracy of segmented crack ima-ges are 72.85％and 85.36％,respectively.Compared with other classical image segmentation models,the proposed network has a significantly more prominent crack detection effect.It can provide an effective techni-cal support for long-term concrete dam apparent crack monitoring.
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