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All Journal Idealis : Indonesia Journal Information System
Daniel Sande Bona
Desain Komunikasi Visual, Institut Seni Budaya Indonesia Tanah Papua, Jayapura, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Perbandingan YOLO26 dan RT-DETR-L pada Deteksi Elemen Visual Manga Menggunakan Dataset Manga109 Daniel Sande Bona; Tindia Febriyati
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3842

Abstract

Automatic comic element detection is a critical prerequisite for manga digital analysis applications such as indexing, translation, and accessibility enhancement. Manga109 is a widely used benchmark for this task, yet the rapid progress of real-time object detectors has not been matched by a systematic comparison among state-of-the-art models in this domain, particularly between YOLO-family detectors employing Small-Target-Aware Label assignment (STAL) and transformer-based detectors such as RT-DETR. This study benchmarks YOLO26 (nano, small, and medium variants) against RT-DETR-L for detecting four comic element classes (panel, character, text, and face) on Manga109. All models were trained for 100 epochs at 1024×1024 pixels and evaluated using mAP and per-size AP following COCO conventions. YOLO26m achieves the best performance (mAP@0.5:0.95 of 0.7471), while RT-DETR-L obtains the lowest (0.7001) despite the largest parameter count and slowest inference. For small text detection, YOLO26m outperforms RT-DETR-L by 42.7% relatively, and the AP gap across object sizes narrows monotonically as model size grows, supporting the STAL design claim. RT-DETR-L also exhibits significant training instability. The main contribution is a systematic, multi-dimensional benchmark of YOLO26 against RT-DETR-L on Manga109 that evidence STAL effectiveness for small-text detection and offers practical guidance for selecting real-time detectors in manga image analysis.
Co-Authors Tindia Febriyati