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 and significantly reduces the training memory footprint. 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 sup-pression (NMS), our detector FCOS outperforms previous anchor-based 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.
沈春华博士现任澳大利亚阿德莱德大学计算机科学学院教授。2011之前在澳大利亚国家信息通讯技术研究院堪培拉实验室的计算机视觉组工作近6年。 目前主要从事统计机器学习以及计算机视觉领域的研究工作。主持多项科研课题，在重要国际学术期刊和会议发表论文200余篇, 其中近一半发表在CVPR, ICCV, ECCV, TPAMI, IJCV。 他在南京大学获得本科及硕士学位，在阿德莱德大学获得计算机视觉方向的博士学位。2012年被澳大利亚研究理事会(Australian Research Council)授予Future Fellowship。