FoveaBox: Beyound Anchor-Based Object Detection

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generali...

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Vydáno v:	IEEE transactions on image processing Ročník 29; s. 7389 - 7398
Hlavní autoři:	Kong, Tao, Sun, Fuchun, Liu, Huaping, Jiang, Yuning, Li, Lei, Shi, Jianbo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	anchor free Anchors Aspect ratio Bells Benchmarks Boxes Detectors Feature extraction foveabox Object detection Object recognition Parameter sensitivity Proposals Sun Training Visualization
ISSN:	1057-7149, 1941-0042
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox.
AbstractList	We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox.
Author	Sun, Fuchun Kong, Tao Liu, Huaping Jiang, Yuning Shi, Jianbo Li, Lei
Author_xml	– sequence: 1 givenname: Tao orcidid: 0000-0002-9412-1457 surname: Kong fullname: Kong, Tao email: taokongcn@gmail.com organization: ByteDance AI Lab, Beijing, China – sequence: 2 givenname: Fuchun surname: Sun fullname: Sun, Fuchun email: fcsun@tsinghua.edu.cn organization: Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China – sequence: 3 givenname: Huaping surname: Liu fullname: Liu, Huaping organization: Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China – sequence: 4 givenname: Yuning surname: Jiang fullname: Jiang, Yuning organization: ByteDance AI Lab, Beijing, China – sequence: 5 givenname: Lei surname: Li fullname: Li, Lei organization: ByteDance AI Lab, Beijing, China – sequence: 6 givenname: Jianbo surname: Shi fullname: Shi, Jianbo organization: GRASP Laboratory, University of Pennsylvania, Philadelphia, PA, USA
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doi: 10.1109/ICCV.2017.89
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Snippet	We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors...
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SubjectTerms	anchor free Anchors Aspect ratio Bells Benchmarks Boxes Detectors Feature extraction foveabox Object detection Object recognition Parameter sensitivity Proposals Sun Training Visualization
Title	FoveaBox: Beyound Anchor-Based Object Detection
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