Semi-Anchored Detector for One-Stage Object Detection
- URL: http://arxiv.org/abs/2009.04989v1
- Date: Thu, 10 Sep 2020 16:57:09 GMT
- Title: Semi-Anchored Detector for One-Stage Object Detection
- Authors: Lei Chen, Qi Qian, Hao Li
- Abstract summary: A standard one-stage detector is comprised of two tasks: classification and regression.
With ResNet-101 as the backbone, the proposed semi-anchored detector achieves 43.6% mAP on COCO data set.
- Score: 24.42034747738174
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A standard one-stage detector is comprised of two tasks: classification and
regression. Anchors of different shapes are introduced for each location in the
feature map to mitigate the challenge of regression for multi-scale objects.
However, the performance of classification can degrade due to the highly
class-imbalanced problem in anchors. Recently, many anchor-free algorithms have
been proposed to classify locations directly. The anchor-free strategy benefits
the classification task but can lead to sup-optimum for the regression task due
to the lack of prior bounding boxes. In this work, we propose a semi-anchored
framework. Concretely, we identify positive locations in classification, and
associate multiple anchors to the positive locations in regression. With
ResNet-101 as the backbone, the proposed semi-anchored detector achieves 43.6%
mAP on COCO data set, which demonstrates the state-of-art performance among
one-stage detectors.
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