IMPLEMENTASI SSD_RESNET50_V1 UNTUK PENGHITUNG KENDARAAN
Abstract
Google has released the Tensorflow Object Detection API to facilitate deep learning application development using the Tensorflow Object Detection API. The TensorFlow Object Detection API is an open-source framework that can be used to develop, train, and deploy object detection models. In this study, the Tensorflow Object Detection API is implemented in a vehicle counter application with the SSD_Resnet50_v1 detection model. From the research that has been done, applications with the detection of the SSD_Resnet50_v1 model get an accuracy of 56.49% in calculating motor-type vehicles and 54.43% for car-type vehicles.
Kata Kunci : SSD_Resnet50_v1, Vehicle Counting, Tensorflow Object Detection API
Google telah merilis Tensorflow Object Detection API untuk mempermudah pengembangan aplikasi Deep learning dengan menggunakan Tensorflow Object Detection API. TensorFlow Object Detection API adalah open source framework yang dapat digunakan untuk mengembangkan, melatih, dan menggunakan model deteksi objek. Pada penelitian ini Tensorflow Object Detection API diimplementasikan pada aplikasi penghitung kendaraan dengan model deteksi SSD_Resnet50_v1. Dari penelitian yang telah dilakukan, aplikasi dengan model deteksi SSD_Resnet50_v1 mendapatkan akurasi sebesar 56,49% dalam menghitung kendaraan berjenis motor dan 54,43% untuk kendaraan berjenis mobil.
Kata Kunci : SSD_Resnet50_v1, penghitung kendaraan, Tensorflow Object Detection API
Full Text:
PDFReferences
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. CVPR 2016.
Huang,aJ.,iRathod,aV.,lSun,LC.,aaZhu,PM.,ZKorattikara,LA.,KFathi,LA., ...ZMurphy,ZK.c(2017).DSpeed/accuracyaatrade-offsaaforamodern convolutionaliobjectidetectors.iProceedingsi– 30th IEEE Conference oniComputeriVisioniandiPatterniRecognition, CVPR 2017, 2017– Janua, 3296–3305. CVPR.2017.351
Implementasi SSD_Resnet50_v1 untuk Vehicle Counting (Muhammad Nur Rizal) |9
Kumpulan Jurnal Ilmu Komputer (KLIK) Volume ISSN:
Liu,iW.,iAnguelov,iD.,iErhan,iD.,iSzegedy,iC.,iReed,iS.,iFu,iC.iY.,i&aBerg, A.iC.i(2016).iSSD:iSingleishotimultiboxIdetector.aLectureiNotes in ComputerlSciencel(IncludingiSubseriesiLecture Notes in Artificial IntelligenceaandiLectureiNotesainaBioinformatics), 9905 LNCS, 21–37. 978-3-319-46448-0_2
Vijay, dkk. (2020). MLPerf Inference Benchmark. arXiv:1911.02549v2 [cs.LG]
DOI: http://dx.doi.org/10.20527/klik.v8i2.383
Copyright (c) 2021 KLIK - KUMPULAN JURNAL ILMU KOMPUTER
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License. View My Stats