Asphalt Road Crack Detection Method Based on Improved DeepLabv3+Network
A semantic segmentation method based on an improved DeepLabv3+network is proposed to address the issues of low detection accuracy and large errors associated with traditional semantic segmentation techniques for detecting asphalt road cracks.In the encoder stage,this method replaces the backbone network Xception of DeepLabv3+with lightweight MobileNetv2,thereby reducing the number of parameters.In the decoder stage,a dual attention mechanism is incorporated to further improve the segmentation accuracy of the network.The Dice loss function is combined with the original cross-entropy loss function to alleviate the imbalance between foreground and background in the sample.Extensive experiments were conducted on real-time road detection data.The results indicate that,compared with the original DeepLabv3+,the average intersection-to-union ratio(mIoU)and average pixel accuracy(mPA)achieved by the proposed method were higher by 8.98％and 17.39％,respectively.As compared with other mainstream semantic segmentation models,the improved DeepLabv3+also exhibits good performance in detecting asphalt road cracks.
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