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

作　　者：Seolhee Kim Sang-Duck Lee 

机构地区：[1]Department of Electrical and Computer Engineering,Sungkyunkwan University,Suwon,16419,Republic of Korea [2]Logistics System Research Division,Korea Railroad Research Institute,Uiwang,16105,Republic of Korea

出　　处：《Computers, Materials & Continua》2024年第7期195-215,共21页计算机、材料和连续体（英文）

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).

摘　　要：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.

关 键 词：Parcel delivery service damage detection damage classification data augmentation generative adversarial network 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]