基于难分样本挖掘的快速区域卷积神经网络目标检测研究

针对经典的快速区域卷积神经网络(Faster RCNN)训练过程存在太多难训练样本、召回率低等问题，该文采用一种基于在线难分样本挖掘技术(OHEM)与负难分样本挖掘(HNEM)技术相结合的方法，通过训练中实时筛选的最大损失值难分样本进行误差传递，解决了模型对难分样本检测率低问题，提高模型训练效率；为更好地提高模型的召回率和模型的泛化性，该文改进了非极大值抑制(NMS)算法，设置了置信度阈值罚函数，又引入多尺度、数据增强等训练方法。最后通过比较改进前后的结果，经敏感性实验分析表明，该算法在VOC2007数据集上取得了较好效果，平均精度均值从69.9%提升到了74.40%，在VOC2012上从70.4%提升到79.3%，验证了该算法的优越性。

Research on Faster RCNN Object Detection Based on Hard Example Mining

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.