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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159776

Title:	Enhancing Fisheye Lens Object Detection with Generative Data Augmentation
Authors:	廖文宏 Liao, Wen-Hung;Cheng, Pin-Chieh
Contributors:	資訊系
Date:	2025-08
Issue Date:	2025-10-03 09:53:31 (UTC+8)
Abstract:	Overhead fisheye cameras offer broad spatial coverage, making them suitable for surveillance in public spaces such as libraries. However, their severe image distortion and the scarcity of publicly available, privacy-compliant datasets hinder effective object detection. This study addresses these challenges through a dual strategy: augmenting data using text-to-image generative models and correcting fisheye distortion via calibrated intrinsic camera parameters. This approach enables robust training on enriched datasets while mitigating geometric artifacts. Experimental results show substantial performance gains over the YOLOv8 baseline, with mAP@0.5 improving from 0.246 to 0.688 and mAP@0.5:0.95 from 0.122 to 0.518. Detection of small objects—such as beverages—improved markedly, with mAP@0.5 rising from 0.507 to 0.795. Furthermore, combining synthetic and real data in training not only enhances generalization but also improves model robustness under challenging visual conditions.
Relation:	Proceedings of the 21st IEEE International Conference on Advanced Visual and Signal-Based Systems, IEEE Signal Processing Society
Data Type:	conference
DOI link:	https://doi.org/10.1109/AVSS65446.2025.11149842
DOI:	10.1109/AVSS65446.2025.11149842
Appears in Collections:	[Department of Computer Science ] Proceedings

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