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US 2022/0044073 A1Published application
Feature pyramids for object detection
AssigneeCarnegie Mellon University
InventorsFangyi Chen, Chenchen Zhu, Zhiqiang Shen, Han Zhang, Marios Savvides
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Abstract
Disclosed herein is an improvement to prior art feature pyramids for general object detection that inserts a simple norm calibration (NC) operation between the feature pyramids and detection head to alleviate and balance the norm bias caused by feature pyramid network (FPN) and which leverages an enhanced multi-feature selective strategy (MS) during training to assign the ground-truth to one or more levels of the feature pyramid.
Claims21
A framework for training an object detector comprising:
- a feature pyramid;
- a norm calibration layer, to generate a norm calibrated feature pyramid from the feature pyramid;
- a localization subnetwork, coupled to the norm calibrated feature pyramid, the localization network generating a multi-level localization map;
- a classification subnetwork, coupled to the norm calibrated feature pyramid, the classification subnetwork generating a multi-level heat map;
- and
- a multi-selection module for collecting losses from the localization map and the heat map and assigning ground truth and ignored areas to one or more levels of the feature pyramid, based on the collected losses.
3 more elements
A method for training an object detector comprising:
- deriving a feature pyramid from an input image;
- performing a norm calibration on the feature pyramid to generate a norm calibrated feature pyramid;
- applying one or more subnetworks to the norm calibrated feature pyramid to generate one or more multi-level feature maps, the one or more feature maps having losses for each feature on each level of the feature maps;
- and
- selecting one or more levels of the feature pyramid to assign detected objects to based on a comparison of the losses from each level of the one or more feature maps.
2 more elements
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