Hybrid Template Matching and Faster R-CNN for Robust Multimodal Object Detection
DOI:
https://doi.org/10.4108/eetismla.9544Keywords:
Attention Mechanism, CNN, Multimodal Data, Object Detection, TransformerAbstract
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.
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