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
The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and p...The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles.With this,we propose a ground-air cooperation(GAC)based business model for parcel delivery in an urban environment.As per our case study using real-world data in Beijing,the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost,but also reduce road traffic by 8.6%,leading to an annual social benefit of 6.67 billion USD for Beijing.The proposed model utilizes the currently“wasted or unused”rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers,thus fundamentally addressing the congestion,noise,pollution,and road wear and tear problems caused by trucks,and bringing in immense social benefit.展开更多
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The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles.With this,we propose a ground-air cooperation(GAC)based business model for parcel delivery in an urban environment.As per our case study using real-world data in Beijing,the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost,but also reduce road traffic by 8.6%,leading to an annual social benefit of 6.67 billion USD for Beijing.The proposed model utilizes the currently“wasted or unused”rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers,thus fundamentally addressing the congestion,noise,pollution,and road wear and tear problems caused by trucks,and bringing in immense social benefit.
