Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images....Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.展开更多
Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequentl...Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.展开更多
In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing ...In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions.The paper proposes a multi-level strategy,designed and implemented on the basis of periodic structural monitoring oriented to a cost-and time-efficient tunnel control plan.Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations.In a supervised learning framework,Ground Penetrating Radar(GPR)profiles and the revealed structural phenomena have been used as input and output to train and test such networks.Image-based analysis and integrative investigations involving video-endoscopy,core drilling,jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database.The degree of detail and accuracy achieved in identifying a structural condition is high.As a result,this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing,and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.展开更多
基金supported by the School Doctoral Fund of Zhengzhou University of Light Industry No.2015BSJJ051.
文摘Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.
基金supported by a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)(https://www.kaia.re.kr/eng/main.do,accessed on 01/06/2024)supported by a Korea Evaluation Institute of Industrial Technology(KEIT)grant funded by the Korean Government(MOTIE)(141518499)(https://www.keit.re.kr/index.es?sid=a2,accessed on 01/06/2024).
文摘Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.
文摘In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions.The paper proposes a multi-level strategy,designed and implemented on the basis of periodic structural monitoring oriented to a cost-and time-efficient tunnel control plan.Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations.In a supervised learning framework,Ground Penetrating Radar(GPR)profiles and the revealed structural phenomena have been used as input and output to train and test such networks.Image-based analysis and integrative investigations involving video-endoscopy,core drilling,jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database.The degree of detail and accuracy achieved in identifying a structural condition is high.As a result,this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing,and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.
