This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
Road damage detection is an important aspect of road maintenance.Traditional manual inspections are laborious and imprecise.With the rise of deep learning technology,pavement detection methods employing deep neural ne...Road damage detection is an important aspect of road maintenance.Traditional manual inspections are laborious and imprecise.With the rise of deep learning technology,pavement detection methods employing deep neural networks give an efficient and accurate solution.However,due to background diversity,limited resolution,and fracture similarity,it is tough to detect road cracks with high accuracy.In this study,we offer a unique,efficient and accurate road crack damage detection,namely YOLOv8-ES.We present a novel dynamic convolutional layer(EDCM)that successfully increases the feature extraction capabilities for small fractures.At the same time,we also present a new attention mechanism(SGAM).It can effectively retain crucial information and increase the network feature extraction capacity.The Wise-IoU technique contains a dynamic,non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely,especially for low-quality samples.We validate our method on both RDD2022 and VOC2007 datasets.The experimental results suggest that YOLOv8-ES performs well.This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
文摘Road damage detection is an important aspect of road maintenance.Traditional manual inspections are laborious and imprecise.With the rise of deep learning technology,pavement detection methods employing deep neural networks give an efficient and accurate solution.However,due to background diversity,limited resolution,and fracture similarity,it is tough to detect road cracks with high accuracy.In this study,we offer a unique,efficient and accurate road crack damage detection,namely YOLOv8-ES.We present a novel dynamic convolutional layer(EDCM)that successfully increases the feature extraction capabilities for small fractures.At the same time,we also present a new attention mechanism(SGAM).It can effectively retain crucial information and increase the network feature extraction capacity.The Wise-IoU technique contains a dynamic,non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely,especially for low-quality samples.We validate our method on both RDD2022 and VOC2007 datasets.The experimental results suggest that YOLOv8-ES performs well.This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
