Computer Science ›› 2018, Vol. 45 ›› Issue (5): 250-254.doi: 10.11896/j.issn.1002-137X.2018.05.043

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Improved Faster RCNN Training Method Based on Hard Negative Mining

AI Tuo, LIANG Ya-ling and DU Ming-hui   

  • Online:2018-05-15 Published:2018-07-25

Abstract: In the training process of object detection method named Faster RCNN(Faster Region-based Convolutional Neural Network),there is a data imbalance problem which means that training data contains an overwhelming number of negative examples.Aiming at this problem,a discriminant function was proposed to distinguish hard positive examples,which combines location loss and classification loss.Based on this function and hard negative mining,an improved bootstrap sampling method was proposed.Five-step training method was proposed by introducing the bootstrap sampling into traditional Faster RCNN training.Comparing with the traditional training,this method improves network’s generalization ability,reduces false positive rate,and can learn hard example better.The experimental results show that the model trained by five step attains 2.4% higher mAP(mean Average Precision) on Pascal VOC 2007 dataset,reduces false positive by 3.2% on FDDB(Face Detection Data Set and Benchmark) with the same true positive rate,and gets higher fitting degree of boundary box.

Key words: Faster RCNN,Object detection,Hard negative mining,Bootstrap sampling

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