Effective Fusion Factor in FPN for Tiny Object Detection

By Yuqi Gong, Yao Ding, Xiaoke Peng, Jian Zhao, Zhenjun Han. IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020

325 citations recorded by the source corpus.

FPN-based detectors have made significant progress in general object detection, e.g., MS COCO and PASCAL VOC. However, these detectors fail in certain application scenarios, e.g., tiny object detection. In this paper, we argue that the top-down connections between adjacent layers in FPN bring two-side influences for tiny object detection, not only positive. We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection. After series of experiments and analysis, we explore how to estimate an effective value of fusion factor for a particular dataset by a statistical method. The estimation is dependent on the number of objects distributed in each layer. Comprehensive experiments are conducted on tiny object detection datasets, e.g., TinyPerson and Tiny CityPersons. Our results show that when configuring FPN with a proper fusion factor, the network is able to achieve significant performance gains over the baseline on tiny object detection datasets. Codes and models will be released.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5af9ac7ce85bb40de6454f59395cea12129726a9

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In Computer Vision and Pattern Recognition

  1. Gong Cheng
  2. Junwei Han
  3. Xiwen Yao
  4. Ke Yan
  5. Xiang Yuan
  6. Qinghua Zeng
  7. Peicheng Zhou
  8. Lei Guo

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