· 2019
FCOS: Fully Convolutional One-Stage Object Detection
- 6,143citations
- 2019year
Short summary
FCOS, a novel anchor-box-free and proposal-free object detection method, achieves 44.7% AP on single-model, single-scale testing, outperforming previous one-stage detectors.
AI-generated from the title and abstract; the full text is not read.
Key points
- FCOS is a fully convolutional one-stage object detector that operates in a per-pixel prediction manner.
- It is the first detector to be completely anchor box-free and proposal-free.
- FCOS simplifies the detection pipeline by removing anchor box-related computations and hyperparameters.
- Achieved 44.7% AP in single-model, single-scale testing using a ResNeXt-64x4d-101 backbone.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2019 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science