Intelligent Recognition Technology of Highway Cracks Based on Semantic Segmentation
Real-time detection and timely treatment of highway cracks are crucially fundamental for vehicle safety.Rapid identification and comparison of cracks is a new method for monitoring the development and changes of geological disasters especially when they induce cracks.Therefore,this study proposed an intelligent recognition method of highway cracks based on semantic segmentation,establishing a model with dataset,neural network,calculation parameters,and evaluation indicators to rapidly identify the cracks.The results show that,firstly,when the neural network Attention U-net built in this study semantically segments highway cracks,the binary cross loss function value and accuracy rate reach 0.008 7 and 0.998 4,respectively.Secondly,compared with traditional algorithms,the semantic segmentation method shows higher accuracy,reliability,and superiority in intelligent recognition of highway cracks,with a Dice similarity coefficient of 0.978.Thirdly,the semantic segmentation method has better robustness and generalization ability to deal with brightness and noise.