Transcription of FCOS: Fully Convolutional One-Stage Object Detection
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FCOS: Fully Convolutional One-Stage Object Detection
FCOS: Fully Convolutional One-Stage Object DetectionZhi TianChunhua Shen Hao ChenTong HeThe University of Adelaide, AustraliaAbstractWe propose a Fully Convolutional One-Stage Object detec-tor (FCOS) to solve Object Detection in a per-pixel predic-tion fashion, analogue to semantic segmentation. Almostall state-of-the-art Object detectors such as RetinaNet, SSD,YOLOv3, and Faster R-CNN rely on pre-defined anchorboxes. In contrast, our proposed detector FCOS is anchorbox free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids thecomplicated computation related to anchor boxes such ascalculating overlapping during training. More importantly,we also avoid all hyper-parameters related to anchor boxes,which are often very sensitive to the final Detection perfor-mance. With the only post-processing non-maximum sup-pression (NMS), FCOS with ResNeXt-64x4d-101 AP with single -model and single -scale testing,surpassing previous One-Stage detectors with the advantageof being much simpler.
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 alterna-
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