Research on Faster RCNN Object Detection Based on Hard Example Mining

Ye ZHANG; Ting XU; Dingzhong FENG; Meixian JIANG; Guanghua WU

doi:10.11999/JEIT180702

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1496-1502

Ye ZHANG, Ting XU, Dingzhong FENG, Meixian JIANG, Guanghua WU. Research on Faster RCNN Object Detection Based on Hard Example Mining[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1496-1502. doi: 10.11999/JEIT180702

Citation:

Ye ZHANG, Ting XU, Dingzhong FENG, Meixian JIANG, Guanghua WU. Research on Faster RCNN Object Detection Based on Hard Example Mining[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1496-1502. doi: 10.11999/JEIT180702

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Research on Faster RCNN Object Detection Based on Hard Example Mining

doi: 10.11999/JEIT180702

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds: The National Natrual Science Foundation of China (51605442), Science Technology Department of Zhejiang Province (LGN18G010002)

Received Date: 2018-07-13
Rev Recd Date: 2019-01-28

Available Online: 2019-02-18

Publish Date: 2019-06-01

Abstract

Abstract

Because of the classic Faster RCNN training proccess with too many difficult training samples and low recall rate problem, a method which combines the techniques of Online Hard Example Mining (OHEM) and Hard Negative Example Mining (HNEM) is adopted, which carries out the error transfer for the difficult samples using its corresponding maximum loss value from real-time filtering. It solves the problem of low detection of hard example and improves the efficiency of the model training. To improve the recall rate and generalization of the model, an improved Non-Maximum Suppression (NMS) algorithm is proposed by setting confidence thresholds penalty function; In addition, multi-scale training and data augmentation are also introduced. Finally, the results before and after improvement are compared: Sensibility experiments show that the algorithm achieves good results in VOC2007 data set and VOC2012 data set, with the mean Average Percision (mAP) increasing from 69.9% to 74.40%, and 70.4% to 79.3% respectively, which demonstrates strongly the superiority of the algorithm.
- Multiple object detection,
- Online Hard Example Mining (OHEM),
- Hard Negative Example Mining (HNEM),
- Deep learning,
- Non-Maximum Suppression (NMS)

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References(15)

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