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YOLO-Based damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

Computers, Materials & Continua 2024年第7期80卷 195-215页

作者： Seolhee Kim Sang-Duck Lee Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Republic of Korea Logistics System Research Division Korea Railroad Research InstituteUiwang16105Republic of Korea

damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics *** can be prevented by detecting and addressing the damage before the parcels reach the ***,various studies have been conducted on deep learning techniques related to the detection of parcel *** study proposes a deep learning-based damage detectionmethod for various types of *** 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 *** 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 ***,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 ***,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was *** model can improve customer satisfaction and reduce return costs for parcel delivery companies.

关键词： Parcel delivery service damage detection damage classification data augmentation generative adversarial network

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

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Frontiers of Structural and Civil Engineering 2022年第2期16卷 214-223页

作者： Bernardino CHIAIA Giulia MARASCO Salvatore AIELLO Department of Structural Geotechnical and Building EngineeringPolytechnic University of TurinTorino10129Italy

In recent years,great attention has focused on the development of automated procedures for infrastructures *** efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural *** 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 *** strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical *** 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 ***-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 *** degree of detail and accuracy achieved in identifying a structural condition is *** 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.

关键词： concrete structure GPR damage classification convolutional neural network transfer learning

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