|Table of Contents|

UnderwaterObjectDetectionBasedontheClass-WeightedYOLONet(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2020年01期
Page:
129-135
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
UnderwaterObjectDetectionBasedontheClass-WeightedYOLONet
Author(s):
ZhuShiwei; HangRenlong; LiuQingshan
CollegeofAutomation,NanjingUniversityofInformationScienceandTechnology,Nanjing210044,China
Keywords:
underwaterobject; YOLO; class-weightedloss; dimensionadaptiveclustering
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2020.01.019
Abstract:
Underwaterobjectsdetectionexistmanyissues,suchasblurimage,variousobjectscales,complexbackgroundandsoon.Inthispaper,weproposeaclass-weightedYOLOnetforunderwaterobjectdetection,inwhichaclass-weightedlossisdesignedtobalancesampleofdifficultysoastoacquirebettereffect.Moreover,adimensionadaptiveclusteringofobjectboxisintroducedtopromotethedetectionperformance.TheexperimentalresultsshowthattheproposedmethodoutperformstothetraditionalYOLOnet,withtheincreasingofthemAPfrom71.2%to74.1%andtherecallfrom71.1%to78.3%,inthetaskofdenseobjectdetectionwhicheveryimagenearlycontained20objects.

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Last Update: 2020-03-15