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Authors: Ala’a Alshubbak 1 ; 2 and Daniel Görges 1

Affiliations: 1 Institute of Electromobility, University of Kaiserslautern-Landau, Kaiserslautern, Germany ; 2 German Jordanian University, Amman, Jordan

Keyword(s): Anchor-Free Object Detection, Deep Learning, ResNet, IOU Losses, Attention Mechanism, Saliency Map.

Abstract: In this paper, an investigation of different IoU loss functions and a spatial attention mechanism within anchor-free object detectors is presented. Two anchor-free dense predictor models are studied: FASF and FCOS models. The models are tested on two different datasets: the benchmark COCO dataset and a small dataset called OPEDD. The results show that some loss functions and using the attention mechanism outperform their original counterparts for both the huge multi-class COCO dataset and the small unity-class dataset of OPEDD. The proposed structure is tested over different backbones: ResNet-50, ResNet-101, and ResNeXt-101. The accuracy of basic models trained over the coco dataset improves by 1.3% and 1.6% mAP for the FSAF and FCOS models based on ResNet-50, respectively. On the other hand, it increases by 2.3% and 15.8% for the same models when trained on the OPEDD dataset. The effect is interpreted using a saliency map.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Alshubbak, A. and Görges, D. (2024). Investigation of the Performance of Different Loss Function Types Within Deep Neural Anchor-Free Object Detectors. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 401-411. DOI: 10.5220/0012354900003636

@conference{icaart24,
author={Ala’a Alshubbak. and Daniel Görges.},
title={Investigation of the Performance of Different Loss Function Types Within Deep Neural Anchor-Free Object Detectors},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={401-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012354900003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Investigation of the Performance of Different Loss Function Types Within Deep Neural Anchor-Free Object Detectors
SN - 978-989-758-680-4
IS - 2184-433X
AU - Alshubbak, A.
AU - Görges, D.
PY - 2024
SP - 401
EP - 411
DO - 10.5220/0012354900003636
PB - SciTePress