Cited by 7,013Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionDomain Adaptation and Few-Shot Learning

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FCOS: Fully Convolutional One-Stage Object Detection

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Tian, Zhi, Shen, Chunhua, Chen, Hao, He, Tong·2019·2019 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2019.00972

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Abstract

We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: https://tinyurl.com/FCOSv1.

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Authors: Tian, Z., Shen, C., Chen, H., & He, T.
Affiliations: The University of Adelaide (AU)
ORCID: Tian, Zhi
Shen, Chunhua
He, Tong
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2019-10
Pages: 9626-9635
DOI: 10.1109/iccv.2019.00972
OpenAlex: W2982770724
Cited by: 7,013
References: 34
Citations by year: 2026: 591 · 2025: 977 · 2024: 1403 · 2023: 1476 · 2022: 1212 · 2021: 921 · 2020: 386 · 2019: 45
FWCI: 209.06
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: cc-by-nc-sa
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: FCOS, one-stage object detection, anchor-free detection, fully convolutional network, non-maximum suppression, instance segmentation

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FCOS: Fully Convolutional One-Stage Object Detection

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