relation: http://repository.upi.edu/135573/
title: PENGEMBANGAN MODEL THERMAL RESIDUALNET-128 UNTUK DETEKSI OBJEK DALAM CITRA TERMAL MENGGUNAKAN PEMBELAJARAN RESIDUAL
creator: Miftahul Falah, -
creator: Galura Muhammad Suranegara, -
creator: Ichwan Nul Ichsan, -
subject: T Technology (General)
description: Deteksi objek untuk kendaraan otonom menghadapi tantangan dalam kondisi pencahayaan rendah. Citra termal menawarkan solusi yang efektif, namun arsitektur Convolutional Neural Network (CNN) konvensional sering mengalami penurunan performa pada jaringan dalam. Penelitian ini bertujuan mengembangkan dan mengevaluasi model Thermal ResidualNet-128 (TRNet-128), arsitektur CNN dengan mekanisme pembelajaran residual untuk mendeteksi objek manusia dan pengendara sepeda motor. Dataset yang digunakan terdiri dari 1.008 gambar citra termal grayscale. Performa model TRNet-128 dievaluasi menggunakan metrik accuracy, precision, recall, F1-score, confusion matrix, dan mean Average Precision (mAP), serta dibandingkan dengan model CNN sederhana tanpa residual block. Hasil menunjukkan model TRNet-128, setelah augmentasi data untuk mengatasi overfitting, mencapai akurasi validasi 99,86% dan mAP 0,99977, jauh lebih tinggi dibandingkan model CNN sederhana yang hanya 0,99652. Kesimpulannya, integrasi residual blocks pada TRNet-128 secara efektif meningkatkan akurasi klasifikasi dan ketepatan deteksi objek dalam citra termal.  -----  Object detection for autonomous vehicles faces challenges in low-light conditions. Thermal imagery offers an effective solution; however, conventional Convolutional Neural Network (CNN) architectures often experience performance degradation in deep networks. This study aims to develop and evaluate the Thermal ResidualNet-128 (TRNet-128) model, a CNN architecture equipped with a residual learning mechanism to detect human and motorcycle rider objects. The dataset used consists of 1,008 grayscale thermal images. The performance of the TRNet-128 model is evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and mean Average Precision (mAP), and compared to a simple CNN model without residual block. Results show that the TRNet-128 model, after data augmentation to mitigate overfitting, achieved a validation accuracy of 99.86% and an mAP of 0.99977, significantly outperforming the simple CNN model, which only reached 0.99652. In conclusion, the integration of residual blocks in the TRNet-128 architecture effectively enhances classification accuracy and object detection precision in thermal images.
date: 2025-07-24
type: Thesis
type: NonPeerReviewed
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identifier: http://repository.upi.edu/135573/8/S_SISTEL_2104755_Title.pdf
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identifier: http://repository.upi.edu/135573/2/S_SISTEL_2104755_Chapter1.pdf
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identifier: http://repository.upi.edu/135573/3/S_SISTEL_2104755_Chapter2.pdf
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identifier: http://repository.upi.edu/135573/4/S_SISTEL_2104755_Chapter3.pdf
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identifier: http://repository.upi.edu/135573/5/S_SISTEL_2104755_Chapter4.pdf
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identifier: http://repository.upi.edu/135573/6/S_SISTEL_2104755_Chapter5.pdf
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identifier: http://repository.upi.edu/135573/7/S_SISTEL_2104755_Appendix.pdf
identifier:   Miftahul Falah, - and Galura Muhammad Suranegara, - and Ichwan Nul Ichsan, -  (2025) PENGEMBANGAN MODEL THERMAL RESIDUALNET-128 UNTUK DETEKSI OBJEK DALAM CITRA TERMAL MENGGUNAKAN PEMBELAJARAN RESIDUAL.  S1 thesis, Universitas Pendidikan Indonesia.   
relation: https://repository.upi.edu/