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Sensors 2018, 18(10), 3415; https://doi.org/10.3390/s18103415

Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

1
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 27 August 2018 / Revised: 2 October 2018 / Accepted: 5 October 2018 / Published: 11 October 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
PDF [2689 KB, uploaded 11 October 2018]

Abstract

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.
Keywords: image object detection; RCNN; Faster RCNN; Light Head RCNN image object detection; RCNN; Faster RCNN; Light Head RCNN
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, J.; Zhang, J.; Yu, S. Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection. Sensors 2018, 18, 3415.

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