DCM-Net:用于复杂环境下的道路裂缝分割算法OA

An improved U-Net-based pavement crack segmentation algorithm named DCM-Net is proposed in response to the challenges posed by complex background noise,intricate crack patterns,and severe mis-segmentation in pavement crack images.A dual-encoder design is adopted in the DCM-Net,and the additional branch mitigates information loss caused by the simple stacking of convolution and pooling in a single branch.CoTAttention mechanism is incorporated into the original skip connections to enhance important features within low-level semantic information and mitigate the impact of background noise,lane markings,manhole covers,and other obstructions,so as to enhance the feature representation of useful information.The convolution module in the original encoder is redesigned.The dilated convolution is introduced to increase the receptive field.The multi-dimensional feature extraction strategy is adopted to improve the feature extraction ability of the model across various crack morphologies.The comparative experimental results show that on the self-built dataset CrackNew,the Dice,mean intersection over union(mIoU),precision,recall rate and F1 of the DCM-Net are improved by 6.3%,5.7%,5.4%,1.8%and 5.3%,respectively,in comparison with those of the UNet.Meanwhile,it is superior to the other mainstream segmentation models.On the publicly available datasets Crack500 and Gaps384,the DCM-Net maintains leading performance across all metrics.Ablation experiments conducted on the dataset DeepCrack confirm the effectiveness of each module of the DCM-Net.In comparison with the other segmentation models,the DCM-Net enhances the segmentation precision for pavement cracks significantly.To sum up,the model can be applied to road crack segmentation in complex environment.