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All Journal Jurnal Ilmu Komputer dan Informasi TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sistem Komputer Jurnal Kelautan Tropis Edu Komputika Journal JOIV : International Journal on Informatics Visualization Jurnal Teknologi Informasi dan Multimedia Jurnal Ilmu Teknik dan Komputer Jurnal Mutiara Pendidikan
Risma Septiana
Departemen Teknik Komputer Universitas Diponegoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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YoloV8, EfficientNetv2, and CSP Darknet Comparison as Recognition Model’s Backbone for Drone-Captured Images Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma; Windasari, Ike Pertiwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2880

Abstract

Artificial intelligence (AI) has recently empowered drones to support smart city apps and recognize on-the-ground objects or events. Various pre-trained backbones are available to develop object recognition models, and some of them could boost the models’ accuracy. Consequently, it becomes difficult for practitioners to select a suitable backbone as a feature extractor during recognition model development. Hence, this research aims to provide a benchmark examining the performance of three popular backbones in supporting recognition models using images captured by drones as the dataset. This research used the UAV-AUAIR dataset and compared three deep learning backbone architectures as the feature extractor, namely YoloV8_s, EfficientNetv2_s, and CSP_DarkNet_l. The head part of each selected backbone was replaced with YoloV8Detector architecture, provided by Keras-CV, to perform the inference tasks. The models generated during training were evaluated against four measurement methods: loss function, intersection over union (IOU), across-scale mean average precision (mAP), and computational performance. The results showed that the model generated using EfficientNetv2_s backbone outperformed the others in most criteria, except the computational performance and detecting small objects, which was won by YOLOV8_s and CSP_Darknet_l, respectively. Thus, EfficientNetv2_s and CSP_DarkNet_l can be considered if app development concerns accuracy. Meanwhile, YoloV8_s is far better when computational performance is essential, as its prediction time achieved 0.8 seconds per image. This study is essential as a reference for practitioners, particularly those who want to develop an object-recognition model based on a pre-trained backbone.
A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1199

Abstract

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.
Co-Authors Agung Budi Prasetijo Ahmad Fashiha Hastawan Ajub Ajulian Z Aryani, Ritme Cahya Astawa, I Made Suwasa Auliya, Novita Chatarina Umbul Wahyuni Dania Eridani Erwin Adriono Fahruddin Gumiwang, Dimas Putu Ike Pertiwi Windasari Maemunah Maemunah Marsha, Veliana Maula, Nurinda Ramadhanty Nur Mishbah Hayat Rinta Kridalukmana Sukiswo Sukiswo Yudi Eko Windarto