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FoveaBox: Beyond Anchor-based Object Detector
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 .Comment: IEEE Transactions on Image Processing, code at:
https://github.com/taokong/FoveaBo
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