Research Article
Hybrid Template Matching and Faster R-CNN for Robust Multimodal Object Detection
@ARTICLE{10.4108/eetismla.9544, author={Hewa Majeed Zangana}, title={Hybrid Template Matching and Faster R-CNN for Robust Multimodal Object Detection}, journal={EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications}, volume={2}, number={1}, publisher={EAI}, journal_a={ISMLA}, year={2025}, month={10}, keywords={Attention Mechanism, CNN, Multimodal Data, Object Detection, Transformer}, doi={10.4108/eetismla.9544} }- Hewa Majeed Zangana
Year: 2025
Hybrid Template Matching and Faster R-CNN for Robust Multimodal Object Detection
ISMLA
EAI
DOI: 10.4108/eetismla.9544
Abstract
This paper introduces a hybrid object detection framework that integrates template matching with the Faster R-CNN deep learning algorithm to improve robustness in challenging conditions such as occlusion, clutter, and low resolution. The novelty of this work lies in systematically combining a traditional template-matching branch with a two-stage detector, enabling the system to capture predefined structural cues alongside learned deep features. The proposed score-based fusion mechanism further refines detections by weighting outputs from both branches. Experimental results on COCO and LASIESTA datasets show that the hybrid model achieves an F1 score of 88.6% and a mAP@0.75 of 69.4%, surpassing both template-only and Faster R-CNN-only baselines. These findings highlight the effectiveness of the hybrid strategy in enhancing detection accuracy and robustness while maintaining practical computational efficiency.
Copyright © 2025 Hewa Majeed Zangana, licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
